Chilean prosecutor’s office Data merge
16 noviembre, 2021
Obtain the database
We recoded the region to lower letters for Metropolitan Region sectors. Additionally, we generated the _simple columns with the dates with a more common format.
library(readxl)
base_fiscalia_orig <- read_excel(gsub("/SUD_CL/Fiscalia_merge.Rmd","/BASE.xlsx",rstudioapi::getSourceEditorContext()$path),
sheet = "BASE", skip = 4, guess_max = min(1000000, 1000000)) %>% janitor::clean_names()library(compareGroups)## Warning: package 'compareGroups' was built under R version 4.0.5base_fiscalia<-
base_fiscalia_orig %>%
dplyr::mutate(gls_region=dplyr::case_when(gls_region=="REGION METROPOLITANA CENTRO NORTE"~ "RM Centro Norte",
gls_region=="REGION METROPOLITANA OCCIDENTE"~ "RM Occidente",
gls_region=="REGION METROPOLITANA ORIENTE"~ "RM Oriente",
gls_region=="REGION METROPOLITANA SUR"~ "RM Sur",
T~NA_character_)) %>%
dplyr::mutate(region_delito=dplyr::case_when(region_delito=="REGION METROPOLITANA CENTRO NORTE"~ "RM Centro Norte",
region_delito=="REGION METROPOLITANA OCCIDENTE"~ "RM Occidente",
region_delito=="REGION METROPOLITANA ORIENTE"~ "RM Oriente",
region_delito=="REGION METROPOLITANA SUR"~ "RM Sur",
T~NA_character_)) %>%
dplyr::mutate(fec_comision_simple=as.Date(stringr::str_extract(as.character(fec_comision), "^.{10}"))) %>%
dplyr::mutate(fec_cbiorelacion_simple=as.Date(stringr::str_extract(as.character(fec_cbiorelacion), "^.{10}"))) %>%
dplyr::mutate(termino_relacion_simple=as.Date(stringr::str_extract(as.character(termino_relacion), "^.{10}")))
no_mostrar=1
if(no_mostrar==0){
CONS_C1_df_dup_SEP_2020_match<-
CONS_C1_df_dup_SEP_2020 %>%
dplyr::filter(dup==1) %>% #, tipo_de_plan_2 %in% c("PG-PR","M-PR","PG-PAI","M-PAI","PG-PAB","M-PAB")
dplyr::mutate(tipo_de_plan_res=dplyr::case_when(grepl("PR",as.character(tipo_de_plan_2))~1,
grepl("PAI",as.character(tipo_de_plan_2))~0,
grepl("PAB",as.character(tipo_de_plan_2))~0,
TRUE~NA_real_)) %>%
dplyr::mutate(tipo_de_plan_res=factor(tipo_de_plan_res)) %>%
dplyr::mutate(abandono_temprano_rec=factor(if_else(as.character(motivodeegreso_mod_imp)=="Early Drop-out",TRUE,FALSE,NA))) %>%
dplyr::mutate(dg_trs_cons_sus_or=factor(if_else(as.character(dg_trs_cons_sus_or)=="Drug dependence",TRUE,FALSE,NA))) %>%
dplyr::mutate(tipo_centro_pub=factor(if_else(as.character(tipo_centro)=="Public",TRUE,FALSE,NA))) %>%
dplyr::mutate(condicion_ocupacional_corr=factor(condicion_ocupacional_corr),cat_ocupacional_corr=factor(cat_ocupacional_corr)) %>%
dplyr::mutate(dg_trs_fis_rec=factor(dplyr::case_when(as.character(diagnostico_trs_fisico)=="En estudio"~"Diagnosis unknown (under study)",as.character(diagnostico_trs_fisico)=="Sin trastorno"~'Without physical comorbidity',cnt_diagnostico_trs_fisico>0 ~'With physical comorbidity',
TRUE~NA_character_)))%>%
dplyr::mutate(escolaridad_rec=parse_factor(as.character(escolaridad_rec),levels=c('3-Completed primary school or less', '2-Completed high school or less', '1-More than high school'), ordered=T,trim_ws=T,include_na =F, locale=locale(encoding = "Latin1"))) %>%
dplyr::mutate(freq_cons_sus_prin=parse_factor(as.character(freq_cons_sus_prin),levels=c('Did not use', 'Less than 1 day a week','2 to 3 days a week','4 to 6 days a week','1 day a week or more','Daily'), ordered =T,trim_ws=T,include_na =F, locale=locale(encoding = "UTF-8"))) %>%
dplyr::mutate(evaluacindelprocesoteraputico=dplyr::case_when(grepl("1",as.character(evaluacindelprocesoteraputico))~'1-High Achievement',grepl("2",as.character(evaluacindelprocesoteraputico))~'2-Medium Achievement',grepl("3",as.character(evaluacindelprocesoteraputico))~'3-Minimum Achievement', TRUE~as.character(evaluacindelprocesoteraputico))) %>%
dplyr::mutate(evaluacindelprocesoteraputico=parse_factor(as.character(evaluacindelprocesoteraputico),levels=c('1-High Achievement', '2-Medium Achievement','3-Minimum Achievement'), ordered =T,trim_ws=T,include_na =F, locale=locale(encoding = "UTF-8"))) %>%
dplyr::select_(.dots = match.on_tot) %>%
dplyr::mutate(more_one_treat=factor(ifelse(duplicates_filtered>1,1,0))) %>%
data.table::data.table()
#attr(CONS_C1_df_dup_SEP_2020_match$sus_ini_mod_mvv,"label")<-"Starting Substance"
}
invisible("Comentarios")
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kableone <- function(x, caption=NULL, col.names=NA, smd=T, test=T, varLabels=T, noSpaces=T, printToggle=T, dropEqual=F, ...) {
capture.output(x <- print(x, smd=T, test=test, varLabels=varLabels,noSpaces=noSpaces, printToggle=printToggle, dropEqual=dropEqual, ...))
knitr::kable(x,format= "html", format.args= list(decimal.mark= ".", big.mark= ","),
caption= caption, col.names= col.names)
}
as.data.frame.TableOne <- function(x, ...) {
capture.output(print(x, showAllLevels = TRUE, smd=T, test=T, varLabels=T, ...) -> x)
y <- as.data.frame(x)
y$characteristic <- na_if(rownames(x), "")
y <- y %>%
#fill(characteristic, .direction = "down") %>%
dplyr::select(characteristic, everything())
rownames(y) <- NULL
y
}
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options(knitr.kable.NA = '')
##
cat("Vector of variables to summarize")## Vector of variables to summarize#obtener las columnas así nos permite descartar nombres errróneos
myVars <-dput(names(dplyr::select(base_fiscalia, -c("rut_enc_saf","ruc","idrelacion","cod_delito","cod_lugarocurrencia","cod_parentescoimputado","cod_mottermino","cod_motsuspension","cod_proctermino","idsujeto_imputado","idsujeto_victima","impcod_tiposujeto","cod_lugarocurrencia","cod_sitiosuceso","cod_comunadelito","cod_region"))))## c("encontrado_como_victima", "encontrado_como_imputado", "tipo_sujeto_vic",
## "gls_tipo_sujeto_vic", "reg", "gls_region", "tipo_termino", "agrupa_terminos",
## "gls_materia", "familia_delito", "relacion_vifsaf", "gls_parentesco",
## "gls_mottermino", "gls_motsuspension", "gls_proctermino", "gls_tipo_imputado",
## "iddelito", "fec_comision", "termino_relacion", "lugar_ocurrencia",
## "gls_sitiosuceso", "gls_comuna", "region_delito", "fec_cbiorelacion",
## "medidas_155", "medidas_pp", "medidas_ip", "marca_suspension_43",
## "marca_pena_44", "marca_multa_45", "medida_alternativa_46", "clasificacion_pena_47",
## "tramos_condena_48", "clasificacion_penarpa_1_49", "clasificacion_penarpa_2_50",
## "marca_suspension_51", "marca_pena_52", "marca_multa_53", "medida_alternativa_54",
## "clasificacion_pena_55", "tramos_condena_56", "clasificacion_penarpa_1_57",
## "clasificacion_penarpa_2_58", "fec_comision_simple", "fec_cbiorelacion_simple",
## "termino_relacion_simple")cat("Vector of categorical variables that need transformation")## Vector of categorical variables that need transformationcatVars <- dput(names(dplyr::select(base_fiscalia,c("encontrado_como_victima", "encontrado_como_imputado", "gls_tipo_sujeto_vic", "gls_region","tipo_termino", "agrupa_terminos","gls_materia","familia_delito","relacion_vifsaf","gls_parentesco","gls_mottermino","gls_motsuspension","gls_proctermino","gls_tipo_imputado","lugar_ocurrencia","gls_sitiosuceso","gls_comuna","region_delito","medidas_155","medidas_pp","medidas_ip","marca_suspension_43","marca_pena_44","marca_multa_45","medida_alternativa_46","clasificacion_pena_47","tramos_condena_48","clasificacion_penarpa_1_49","clasificacion_penarpa_2_50","marca_suspension_51","marca_pena_52","marca_multa_53","medida_alternativa_54","clasificacion_pena_55","tramos_condena_56","clasificacion_penarpa_1_57","clasificacion_penarpa_2_58"))))## c("encontrado_como_victima", "encontrado_como_imputado", "gls_tipo_sujeto_vic",
## "gls_region", "tipo_termino", "agrupa_terminos", "gls_materia",
## "familia_delito", "relacion_vifsaf", "gls_parentesco", "gls_mottermino",
## "gls_motsuspension", "gls_proctermino", "gls_tipo_imputado",
## "lugar_ocurrencia", "gls_sitiosuceso", "gls_comuna", "region_delito",
## "medidas_155", "medidas_pp", "medidas_ip", "marca_suspension_43",
## "marca_pena_44", "marca_multa_45", "medida_alternativa_46", "clasificacion_pena_47",
## "tramos_condena_48", "clasificacion_penarpa_1_49", "clasificacion_penarpa_2_50",
## "marca_suspension_51", "marca_pena_52", "marca_multa_53", "medida_alternativa_54",
## "clasificacion_pena_55", "tramos_condena_56", "clasificacion_penarpa_1_57",
## "clasificacion_penarpa_2_58")## Create a TableOne object
tab1<- as.data.frame.TableOne(CreateTableOne(vars = myVars, data = base_fiscalia, factorVars = catVars,
smd=T, includeNA=T,test=T))## Warning in ModuleReturnVarsExist(vars, data): These variables only have NA/
## NaN: tramos_condena_48 clasificacion_penarpa_1_49 clasificacion_penarpa_2_50
## tramos_condena_56 clasificacion_penarpa_1_57 clasificacion_penarpa_2_58 Dropped## Warning in ModuleReturnVarsExist(factorVars, data): These variables only have
## NA/NaN: tramos_condena_48 clasificacion_penarpa_1_49 clasificacion_penarpa_2_50
## tramos_condena_56 clasificacion_penarpa_1_57 clasificacion_penarpa_2_58 Dropped## Warning in CreateTableOne(vars = myVars, data = base_fiscalia, factorVars = catVars, : Dropping variable(s) fec_comision termino_relacion fec_cbiorelacion fec_comision_simple fec_cbiorelacion_simple termino_relacion_simple due to unsupported class.kableone(tab1, size=11, first.strip=T, hide.no="no",
format="html",caption= "Table 1. Summary descriptives",
smd=T, test=T, varLabels=T,noSpaces=T, printToggle=T, dropEqual=F) %>% #,col.names=c("Variables","Residential", "Ambulatory", "p-value")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover","condensed"),font_size= 10) %>%
row_spec(1, bold=T, italic=T,color="black",hline_after=T,extra_latex_after="\\arrayrulecolor{white}",font_size= 11) %>%
kableExtra::add_footnote(c("Note. Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;", "Categorical variables are presented as number (%)"), notation = "none")%>%
kableExtra::scroll_box(width = "100%", height = "375px")| characteristic | level | Overall |
|---|---|---|
| n | 568297 | |
| encontrado_como_victima (%) | NO | 376766 ( 66.3) |
| SI | 191531 ( 33.7) | |
| encontrado_como_imputado (%) | NO | 191165 ( 33.6) |
| SI | 377132 ( 66.4) | |
| tipo_sujeto_vic (%) | 1 | 306 ( 0.1) |
| 15 | 226 ( 0.0) | |
| 19 | 330 ( 0.1) | |
| 2 | 2 ( 0.0) | |
| 3 | 13 ( 0.0) | |
| 5 | 4 ( 0.0) | |
| 6 | 549362 ( 96.7) | |
| 8 | 1 ( 0.0) | |
| 18053 ( 3.2) | ||
| gls_tipo_sujeto_vic (%) | ABOGADO QUERELLANTE | 1 ( 0.0) |
| DENUNCIADO | 2 ( 0.0) | |
| DENUNCIANTE | 306 ( 0.1) | |
| IMPUTADO | 13 ( 0.0) | |
| QUERELLANTE | 4 ( 0.0) | |
| TESTIGO | 226 ( 0.0) | |
| VICTIMA | 549362 ( 96.7) | |
| VICTIMA 108i.2 | 330 ( 0.1) | |
| 18053 ( 3.2) | ||
| reg (mean (SD)) | 10.70 (5.48) | |
| gls_region (%) | RM Centro Norte | 78621 ( 13.8) |
| RM Occidente | 63855 ( 11.2) | |
| RM Oriente | 44119 ( 7.8) | |
| RM Sur | 63921 ( 11.2) | |
| 317781 ( 55.9) | ||
| tipo_termino (%) | OTROS T¿RMINOS | 32066 ( 5.6) |
| SALIDA JUDICIAL | 305696 ( 53.8) | |
| SALIDA NO JUDICIAL | 230514 ( 40.6) | |
| 21 ( 0.0) | ||
| agrupa_terminos (%) | ACUERDO REPARATORIO | 13679 ( 2.4) |
| AGRUPACI¿N A OTRO CASO | 28280 ( 5.0) | |
| ANULACI¿N ADMINISTRATIVA | 1608 ( 0.3) | |
| ARCHIVO PROVISIONAL | 149674 ( 26.3) | |
| DECISI¿N DE NO PERSEVERAR | 33205 ( 5.8) | |
| FACULTAD PARA NO INVESTIGAR | 42251 ( 7.4) | |
| INCOMPETENCIA | 2433 ( 0.4) | |
| OTRAS CAUSALES DE SUSPENSI¿N | 918 ( 0.2) | |
| OTRAS CAUSALES DE T¿RMINO | 1260 ( 0.2) | |
| PRINCIPIO DE OPORTUNIDAD | 45202 ( 8.0) | |
| SENTENCIA DEFINITIVA ABSOLUTORIA | 11852 ( 2.1) | |
| SENTENCIA DEFINITIVA CONDENATORIA | 157189 ( 27.7) | |
| SOBRESEIMIENTO DEFINITIVO | 28288 ( 5.0) | |
| SOBRESEIMIENTO DEFINITIVO 240 | 44628 ( 7.9) | |
| SOBRESEIMIENTO TEMPORAL | 657 ( 0.1) | |
| SUSPENSI¿N CONDICIONAL DEL PROCEDIMIENTO | 7152 ( 1.3) | |
| 21 ( 0.0) | ||
| gls_materia (%) | ABANDONO DE ARMAS O ELEMENTOS SUJETAS A CONTROL. ART. 14 A. | 8 ( 0.0) |
| ABANDONO DE CÓNYUGE O DE PARIENTES ENFERMOS. ART. 352. | 13 ( 0.0) | |
| ABANDONO DE NIÑOS | 41 ( 0.0) | |
| ABANDONO O MALTRATO ANIMAL ART.291 BIS. | 205 ( 0.0) | |
| ABIGEATO | 251 ( 0.0) | |
| ABORTO COMETIDO POR FACULTATIVO POR CAUSALES NO REGULADAS | 1 ( 0.0) | |
| ABORTO CONSENTIDO POR CAUSLAES N/REGULADAS ART 342 N 3 Y 344 | 21 ( 0.0) | |
| ABORTO SIN CONSENTIMIENTO | 17 ( 0.0) | |
| ABUSO DE FIRMA EN BLANCO | 8 ( 0.0) | |
| ABUSO SEX C/CONTACTO CORP. A MENOR DE 14 AÑOS ART 366 BIS | 1061 ( 0.2) | |
| ABUSO SEX MAYOR 14/MENOR 18 CON CIRCUNS ESTUPRO ART 366 INC2 | 181 ( 0.0) | |
| ABUSO SEX MAYOR DE 14 AÑOS POR SORPRESA Y/O S/CONSENTIM. ART | 1 ( 0.0) | |
| ABUSO SEX. S/CONTAC MAYOR 14 MENOR 18 ART 366 QUAT INC 3,4,5 | 314 ( 0.1) | |
| ABUSO SEX. S/CONTACTO CORP. MENOR 14 ART 366 QUAT. INC 1 Y 2 | 603 ( 0.1) | |
| ABUSO SEXUAL ADULTO | 4 ( 0.0) | |
| ABUSO SEXUAL CALIFICADO (CON OBJETOS O ANIMALES)ART.365 BIS | 36 ( 0.0) | |
| ABUSO SEXUAL DE MAYOR DE 14 (CON CIRC. DE VIOLACIÓN) ART 366 | 955 ( 0.2) | |
| ABUSOS CONTRA PARTICULARES | 2 ( 0.0) | |
| ABUSOS CONTRA PARTICULARES.ARTS. 255. | 320 ( 0.1) | |
| ACCESO, DIVULG.Y USO INDEB.INF.GENÉTICA CON OCASIÓN TRAT.ADN | 1 ( 0.0) | |
| ACCIDENTE CON RESUL. MUERTE O LES.GRAVES ART.196 C LEY 18290 | 475 ( 0.1) | |
| ADQ. O ALMACENAMIENTO MAT.PORNOGRÁFICO INF.ART.374 BIS INC 2 | 19 ( 0.0) | |
| ALLANAMIENTOS IRREGULARES | 11 ( 0.0) | |
| AMENAZA A FISCAL O DEFENSOR EN DESEMP. DE FUNC. ART 268 QUIN | 10 ( 0.0) | |
| AMENAZA CON ARMA (FALTA) ART. 494 Nº4 CÓDIGO PENAL | 1723 ( 0.3) | |
| AMENAZA GENDARME EN DESEMP. DE FUNC. ART 15 D DL 2589 | 214 ( 0.0) | |
| AMENAZAR U OFENDER A FUNC DE INVESTIGACIONES. ART. 17 quáter | 110 ( 0.0) | |
| AMENAZAS A CARABINEROS | 1564 ( 0.3) | |
| AMENAZAS CONDIC. CONTRA PERSONAS Y PROP. ART. 296 1 y 2, 297 | 8342 ( 1.5) | |
| AMENAZAS DE ATENTADOS CONTRA PERSONAS Y PROPIEDADES | 1308 ( 0.2) | |
| AMENAZAS SIMPLES CONTRA PERSONAS Y PROPIEDADES ART. 296 Nº3. | 81517 ( 14.3) | |
| APERTURA, REG. O INTERCEPTACIÓN DE CORRESP. ART.146 Y 156 | 15 ( 0.0) | |
| APREMIOS ILEG. COMETIDOS POR EMPL. PÚBLICOS. (ART. 150 D) | 283 ( 0.0) | |
| APREMIOS ILEGÍTIMOS CON CUASIDELITO (ART. 150 E N° 3) | 6 ( 0.0) | |
| APROPIACIÓN Y DISTRACCIÓN DE COTIZACIONES PREVISIONALES | 918 ( 0.2) | |
| APROPIACIÓN DE CABLES DE TENDIDO ELÉC.O DE COM.ART.443 INC 2 | 42 ( 0.0) | |
| APROPIACIÓN INDEBIDA (INCL. DEPOSITARIO ALZADO) ART.470 Nº1 | 733 ( 0.1) | |
| APROPIACION INDEBIDA ART.470 N°1 | 4302 ( 0.8) | |
| ARROJAMIENTO DE PIEDRAS U OTROS OBJETOS (496 Nº26 Cód.Penal) | 253 ( 0.0) | |
| ASOCIACION ILICITA ART. 28 LEY 19.913 | 4 ( 0.0) | |
| ASOCIACIONES ILÍCITAS LEY DE DROGAS ART. 16 LEY Nº 20.000. | 13 ( 0.0) | |
| ASOCIACIONES ILÍCITAS. ARTS. 292 AL 295 BIS. | 16 ( 0.0) | |
| ATENTADO A VEH.MOT. EN CIRC. CON OBJ.CONTUNDENTE U OTRO SEM. | 107 ( 0.0) | |
| ATENTADO CONTRA JEFE DE ESTADO O AUT. PÚB. ART.2 Nº3 L.18314 | 5 ( 0.0) | |
| ATENTADO EXPLOSIVO O INCENDIARIO | 4 ( 0.0) | |
| ATENTADOS Y AMENAZAS CONTRA LA AUTORIDAD. ART. 261 Nº1 Y 264 | 252 ( 0.0) | |
| AUXILIO AL SUICIDIO | 13 ( 0.0) | |
| BIGAMIA | 1 ( 0.0) | |
| CALUMNIA (ACCIÓN PRIVADA). ART. 412 AL 415. | 14 ( 0.0) | |
| CASTRACIÓN Y MUTILACIÓN | 1 ( 0.0) | |
| CAUSAR LA MUERTE FUNC PDI ART. 17 LOC PDI | 2 ( 0.0) | |
| CAZA Y COMERCIALIZACIÓN DE ESPECIES PROH. (Art.31 Ley 19473) | 7 ( 0.0) | |
| CELEBRACIÓN DE CONTRATO SIMULADO. ART. 471 Nº 2. | 5 ( 0.0) | |
| COHECHO COMETIDO POR EMPLEADO PÚBLICO ART 248, 248 BIS Y 249 | 59 ( 0.0) | |
| COHECHO O SOBORNO COMETIDO POR PARTICULAR. ART. 250 | 24 ( 0.0) | |
| COMERCIALIZACIÓN MAT. PORNOGRÁFICO ELAB.UTIL. MEN.DE 18 AÑOS | 1 ( 0.0) | |
| COMERCIO CLANDESTINO | 10 ( 0.0) | |
| CONDUC BAJO INFLUENCIA DEL ALCOHOL ART.196 C LEY 18290 TRÁNS | 3 ( 0.0) | |
| CONDUC EBRIDAD C/LESIONS MENOS GRAVESART196 INC2 LEY TRAN | 111 ( 0.0) | |
| CONDUC EBRIEDAD C/LESIONS GRAVES ART 196 INC2LEY TRANS | 143 ( 0.0) | |
| CONDUC EBRIEDAD C/LESIONS GRAVÍSIMAS ART196 INC3LEY TRANS | 22 ( 0.0) | |
| CONDUC EBRIEDAD C/RESULT MUERTE ART196INC3 LEY TRANSITO | 29 ( 0.0) | |
| CONDUC EBRIEDAD C/SUSP LICENCIA ART 196,209 LEY TRANSITO | 215 ( 0.0) | |
| CONDUC. BAJO LA INFLUENCIA DEL ALCOHOL CAUSANDO LES.MEN.GRAV | 86 ( 0.0) | |
| CONDUC. INFLUENCIA ALCOHOL CAUSANDO LES.GRAVÍSIMAS O MUERTE | 16 ( 0.0) | |
| CONDUCC. INF. ALCOHOL CON O SIN DAÑO O LES. LEVE 193 INC 1 | 1045 ( 0.2) | |
| CONDUCCIÓN BAJO LA INFLUENCIA DEL ALCOHOL CAUSANDO LES.GRAV | 111 ( 0.0) | |
| CONDUCCIÓN EBRIEDAD CON O SIN DAÑO O LES. LEVE 196 INC. 1 | 9057 ( 1.6) | |
| CONDUCCIÓN ESTADO DE EBRIEDAD CON RESULTADO DE DAÑOS. | 637 ( 0.1) | |
| CONDUCCIÓN ESTADO DE EBRIEDAD CON RESULTADO DE LESIONES. | 921 ( 0.2) | |
| CONDUCCIÓN ESTADO DE EBRIEDAD CON RESULTADO DE MUERTE | 79 ( 0.0) | |
| CONDUCCIÓN SIN LA LICENCIA DEBIDA ART 196 D LEY 18.290 | 1451 ( 0.3) | |
| CONDUCIR INFL ALCOHOL CAUSANDO LES GRAVÍSIMAS ART 193 INC 4 | 1 ( 0.0) | |
| CONDUCIR INFL ALCOHOL CAUSANDO MUERTE. ART 193 INC 4 | 3 ( 0.0) | |
| CONDUCIR VEHICULO CON SANCIÓN VIGENTE. ART.209 LEY 18.290. | 310 ( 0.1) | |
| CONNIVENCIA FUGA Y EVASIÓN CULPABLE DETENIDO ART. 299 A 304 | 5 ( 0.0) | |
| CONSUMO DE BEBIDAS ALCOHÓLICAS EN LA VÍA PÚBLICA | 2 ( 0.0) | |
| CONSUMO DE DROGAS (ART. 41) | 36 ( 0.0) | |
| CONSUMO/PORTE DE DROGAS EN LUGARES CALIFICADOS (ART.51) | 5191 ( 0.9) | |
| CONSUMO/PORTE EN LUG. PÚB.O PRIV. CON PREV.CONCIERTO(ART.50) | 22344 ( 3.9) | |
| CONTRA SALUD PÚBLICA. ARTS. 313 D AL 315 y ART. 317. | 7 ( 0.0) | |
| CONTRA SALUD PUBLICA. ARTS. 313 A Y 313 B | 8 ( 0.0) | |
| CONTRA SALUD PÚBLICA. ARTS. 313 AL 318. | 9 ( 0.0) | |
| CONTRABANDO DE ESPECIES EXÓTICAS (ART. 11, LEY 20.962) | 1 ( 0.0) | |
| CONTRABANDO. INFRACCIÓN A ORDZA ADUANAS ART 168 LEY 20780 | 133 ( 0.0) | |
| CORTE DESTR. ARBOLES RGMTO ART. 21 DCTO.4363 1931 Y L. BOSQ. | 4 ( 0.0) | |
| CUASIDELITO DE HOMICIDIO COMETIDO POR PROFES. DE LA SALUD | 18 ( 0.0) | |
| CUASIDELITO DE HOMICIDIO. CÓDIGO AGRUPADOR. ART. 492 INC. 2º | 233 ( 0.0) | |
| CUASIDELITO DE LESIONES COMETIDOS POR PROFES. DE LA SALUD | 29 ( 0.0) | |
| CUASIDELITO DE LESIONES. ART 490, 491 INC 2° Y 492. | 3515 ( 0.6) | |
| CUASIDELITO VEHICULO MOTORIZADO LEY TRANSITO | 74 ( 0.0) | |
| CULTIVO/COSECHA ESPEC. VEGETALES PRODUCTORAS ESTUPEF (ART.8) | 630 ( 0.1) | |
| DAÑO FALTA (495 N° 21 Código Penal) | 1895 ( 0.3) | |
| DAÑOS A MONUMENTOS NACIONALES ART.38 LEY 17.288 | 1 ( 0.0) | |
| DAÑOS CALIFICADOS. ART. 485 Y 486 | 434 ( 0.1) | |
| DAÑOS O APROPIACIÓN SOBRE MONUM. NAC. ART. 38-38 BIS L.17288 | 17 ( 0.0) | |
| DAÑOS SIMPLES. ART. 487. | 14896 ( 2.6) | |
| DECLARACION MALICIOSA IMPSTOS 97N4(EXCEPTO INC3)COD TRIB | 1 ( 0.0) | |
| DEJAR ANIMALES SUELTOS (496 Nº 17 Código Penal) | 311 ( 0.1) | |
| DELITO DESÓRDENES PÚBLICOS ART. 269 (NO FALTA DEL CÓD 13035) | 2488 ( 0.4) | |
| DELITOS C/ LA LIBERTAD AMBULATORIA Y DERECHO DE ASOCIACIÓN | 1 ( 0.0) | |
| DELITOS C/ LA VIDA Y PRIVACIDAD DE CONVERSACIONES 161 A Y B | 30 ( 0.0) | |
| DELITOS CONTEMPLADOS EN OTROS TEXTOS LEGALES | 84 ( 0.0) | |
| DELITOS CONTENIDOS EN LEYES DE PRENDA ESP. L.20190 Y 18690 | 3 ( 0.0) | |
| DELITOS CONTRA LA LEY DE BOSQUE NATIVO. LEY 20.283 | 3 ( 0.0) | |
| DELITOS CONTRA LEY DE PROPIEDAD INDUSTRIAL | 1 ( 0.0) | |
| DELITOS CONTRA LEY DE PROPIEDAD INTELECTUAL | 41 ( 0.0) | |
| DELITOS DE LA LEY DE SOCIEDADES ANÓNIMAS ART. 134 LEY 18.046 | 1 ( 0.0) | |
| DELITOS MARCARIOS | 3 ( 0.0) | |
| DELITOS QUE CONTEMPLA EL CÓDIGO TRIBUTARIO. ARTS. 97 AL 114 | 20 ( 0.0) | |
| DEMÁS DELITOS CONTRA LEY DE PROPIEDAD INDUSTRIAL | 306 ( 0.1) | |
| DEPOSITARIO ALZADO ART. 444 CPC | 15 ( 0.0) | |
| DESACATO (Art. 240 Código de Procedimiento Civil) | 12125 ( 2.1) | |
| DESATENDER EL LLAMADO A RECLUTAMIENTO DL 2306. Art. 72 | 117 ( 0.0) | |
| DESÓRDENES EN ESPECTÁCULOS PÚBLICOS 494 Nº1 CÓDIGO PENAL | 353 ( 0.1) | |
| DESTRUCCIÓN O ALTERACIÓN DE DESLINDES. ART. 462. | 4 ( 0.0) | |
| DETENCION, DESTIERRO O ARRESTO IRREGULAR ART. 148 | 51 ( 0.0) | |
| DEUDOR/GTE/OTROS PERJUDICAN ACREEDORES Art463, 463bis/ter | 2 ( 0.0) | |
| DISENSIONES DOMÉSTICAS 495 Nº6 CÓDIGO PENAL | 71 ( 0.0) | |
| DISPAROS INJUSTIFICADOS EN VÍA PÚBLICA (ART 14 D INC. FINAL) | 17 ( 0.0) | |
| EJERCICIO ILEGAL DE LA PROFESIÓN. ART. 213 INC 1 | 39 ( 0.0) | |
| ELABORACIÓN ILEGAL DE DROGAS O SUST. SICOT. ART.1 L.20000 | 7 ( 0.0) | |
| ENSEÑANZA NO AUTORIZ. DE ARTES MARCIALES (Art. 5 Ley 18.356) | 2 ( 0.0) | |
| ESPIONAJE INFORMÁTICO ART. 2 Y 4 LEY 19223 | 42 ( 0.0) | |
| ESTAFAS Y OTRAS DEFRAUDACIONES CONTRA PARTICULARES | 6868 ( 1.2) | |
| ESTUPRO | 201 ( 0.0) | |
| EXACCIONES ILEGALES COMETIDAS POR FUNC. PÚB. ART.157 y 241. | 3 ( 0.0) | |
| EXACCIONES ILEGALES COMETIDAS POR PARTICULARES | 9 ( 0.0) | |
| EXPENDIO DE BEBIDAS ALCOHÓLICAS A MENORES. ART.42 LEY 19925 | 38 ( 0.0) | |
| EXTORSION. ART. 438. | 13 ( 0.0) | |
| EXTRANJ INGRESAN/TRATAN EGRESAR CLANDESTINAMENTE 69 DL1094 | 1 ( 0.0) | |
| FABRICACIÓN, ACOPIO O COMERCIALIZACIÓN DE HILO CURADO. | 1 ( 0.0) | |
| FALSA ALARMA INCENDIO, EMERGENCIA O CALAMIDAD ART 268 BIS | 15 ( 0.0) | |
| FALSIF LICENCIAS MEDICAS O PENSIONES. ART.202 INC.2° Y 3° | 5 ( 0.0) | |
| FALSIF MONEDA (PARA BILLETES CÓD 12031).ART 162 AL 214 CP | 54 ( 0.0) | |
| FALSIF OBRAS PROTEGIDAS LEY PROP INTELECT ART.79 BIS | 45 ( 0.0) | |
| FALSIF.PLACAS U OTROS DE INVESTIGACIONES. ART. 16 inciso 3 | 8 ( 0.0) | |
| FALSIF/USO PASAPORTE O PERMISOS PORTE ARMAS ART 199 A 201 | 1 ( 0.0) | |
| FALSIFICACIÓN DE BILLETES ART. 64 LEY ORGÁNICA B.CENTRAL | 131 ( 0.0) | |
| FALSIFICACIÓN LIC.DE COND. Y OTRAS FALSIF. ART.196 B L.18290 | 211 ( 0.0) | |
| FALSIFICACIÓN O USO MALICIOSO DE DOC. PRIVADOS ART.197 Y 198 | 1791 ( 0.3) | |
| FALSIFICACION O USO MALICIOSO DE DOCUMENTOS PUBLICOS | 536 ( 0.1) | |
| FALSO TEST, PERJURIO O DEN.CALUM. ART.206,209,210,211 Y 212 | 68 ( 0.0) | |
| FALTA DE RESPETO A LA AUTORIDAD PÚBLICA 495 Nº4 CÓDIGO PENAL | 910 ( 0.2) | |
| FEMICIDIO INTIMO ART. 390 BIS | 48 ( 0.0) | |
| FINGIMIENTO DE CARGOS O PROFESIONES. ART. 213 INC. 2 | 8 ( 0.0) | |
| FRAUDE ADUANERO. INFRACCIÓN ORDZA ADUANAS ART 169 LEY 20780 | 5 ( 0.0) | |
| FRAUDE DE SUBVENCIONES ART 470 N°8 | 11 ( 0.0) | |
| FRAUDES AL FISCO Y ORGANISMOS DEL ESTADO. ART.239 | 5 ( 0.0) | |
| GANADO QUE ENTRA A PREDIO AJENO CAUSANDO DAÑOS | 5 ( 0.0) | |
| GIRO DOLOSO CHEQUES (CUENTA CERRADA) ART. 22. DFL 707 | 1 ( 0.0) | |
| GIRO DOLOSO CHEQUES (FALTA FONDOS). ART. 22. DFL 707 | 2 ( 0.0) | |
| GIRO DOLOSO DE CHEQUES | 94 ( 0.0) | |
| GIRO DOLOSO DE CHEQUES AC. PENAL PÜBLICA. ART. 42. DFL 707 | 2 ( 0.0) | |
| HALLAZGO DE DROGAS | 126 ( 0.0) | |
| HALLAZGO DE VEHÍCULO | 1374 ( 0.2) | |
| HOMICIDIO | 898 ( 0.2) | |
| HOMICIDIO CALIFICADO | 57 ( 0.0) | |
| HOMICIDIO EN RIÑA O PELEA | 29 ( 0.0) | |
| HOMICIDIO GENDARME ENDESEMP. DE FUNC. ART 15 A - DL 2859 | 1 ( 0.0) | |
| HURTO AGRAVADO (ART. 447 CÓDIGO PENAL) | 856 ( 0.2) | |
| HURTO DE BIENES PERTENECIENTES A REDES DE SUMINISTRO PÚBLICO | 136 ( 0.0) | |
| HURTO DE HALLAZGO | 256 ( 0.0) | |
| HURTO FALTA 494 BIS CÓDIGO PENAL | 36492 ( 6.4) | |
| HURTO SIMPLE | 209 ( 0.0) | |
| HURTO SIMPLE POR UN VALOR DE 4 A 40 UTM | 12888 ( 2.3) | |
| HURTO SIMPLE POR UN VALOR DE MEDIA A 4 UTM | 36454 ( 6.4) | |
| HURTO SIMPLE POR UN VALOR SOBRE 40 UTM | 1289 ( 0.2) | |
| IMPEDIR EJER. DE FUNC. A INSP. MUNIC. ART 496 N° 3 C. PENAL | 2 ( 0.0) | |
| INCENDIO | 1 ( 0.0) | |
| INCENDIO CON PELIGRO PARA LAS PERSONAS (475 Y 476 N° 1 y 2) | 287 ( 0.1) | |
| INCENDIO CON RESULTADO DE MUERTE Y/O LESIONES | 155 ( 0.0) | |
| INCENDIO DE BOSQUES Art. 476 Nº 3 Y 4. | 84 ( 0.0) | |
| INCENDIO SOLO CON DAÑOS O SIN PELIGRO DE PROP. ART.477 Y 478 | 793 ( 0.1) | |
| INCESTO | 9 ( 0.0) | |
| INDUCIR A UN MENOR A ABANDONAR EL HOGAR | 53 ( 0.0) | |
| INFANTICIDIO | 2 ( 0.0) | |
| INFRAC EN PRESTACION SALUD ART 168,174 INC FINAL DS 1 SALUD | 3 ( 0.0) | |
| INFRAC LEY ORG.CONSTITUCIONAL SOBRE VOTAC POPULARES Y ESCRUT | 7 ( 0.0) | |
| INFRAC.A LA SEG.SOC. DL3500 Y 869 LS.18020,18469,18933,19728 | 3 ( 0.0) | |
| INFRACC. LEY GRAL DE TELECOM ART 36 B LETRAS A, B, C Y D | 19 ( 0.0) | |
| INFRACCIÓN A LA LEY ELECTORAL. ART. 128 AL 141. | 6 ( 0.0) | |
| INFRACCIÓN ARTÍCULO 454 | 469 ( 0.1) | |
| INFRACCIÓN LEY 11.564 DE MATADEROS CLANDESTINOS. ART. 1. | 21 ( 0.0) | |
| INFRACCIÓN LEY 18.892 DE PESCA. ART.135 AL 139. | 14 ( 0.0) | |
| INFRACCIÓN NORMAS INHUMACIONES Y EXHUMAC. ART.320,321 Y 322 | 3 ( 0.0) | |
| INFRACCIÓN ORDENANZA ADUANAS (FRAUDE Y CONTRABANDO) ART.176. | 157 ( 0.0) | |
| INFRACCION LEY DE QUIEBRAS. ART.218 AL 221. | 1 ( 0.0) | |
| INFRACCIONES A LA LEY DE SEG.NUCLEAR (Art.41 a 47 Ley 18302) | 6 ( 0.0) | |
| INFRACCIONES AL CÓD. AERONÁUTICO (Arts. 190, 194 bis, 195 a | 4 ( 0.0) | |
| INJURIA (ACCIÓN PRIVADA). ART. 416 AL 420. | 20 ( 0.0) | |
| INJURIAS Y CALUMNIAS POR MEDIOS DE COMUNICACIÓN SOCIAL | 15 ( 0.0) | |
| INSOLVENCIA PUNIBLE (ALZAMIENTO DE BIENES) | 1 ( 0.0) | |
| INSTAL.INDEB.SEÑALES DE TRÁN.O BARRERAS ART.196 A1 LEY 18290 | 1 ( 0.0) | |
| INTERRUPCIÓN SERV. ELECT.(Art.134 DFL.1,1982,Ley Serv.Eléc.) | 2 ( 0.0) | |
| INUTILIZAR DISPOSIT MONITOREO TELEMAT ART 23 SEXIES L18216 | 1 ( 0.0) | |
| LAVADO DE DINERO PERSONA NATURAL ART. 27 LEY 19.913 | 5 ( 0.0) | |
| LESIONES GRAVES | 7069 ( 1.2) | |
| LESIONES GRAVES GRAVÍSIMAS. ART. 397 Nº1 | 129 ( 0.0) | |
| LESIONES LEVES | 53786 ( 9.5) | |
| LESIONES LEVES 494 Nº 5 CODIGO PENAL | 597 ( 0.1) | |
| LESIONES MENOS GRAVES | 54794 ( 9.6) | |
| LESIONES O DAÑOS EN FÚTBOL PROFESIONAL ART 12 INC 1 19327 | 9 ( 0.0) | |
| LEY RESPONSABILIDAD PENAL PERSONAS JURIDICAS LEY 20393 | 2 ( 0.0) | |
| LOTEOS IRREGULARES (Art.138 DFL 458,1975,Ley Gral.Urb.y Con) | 2 ( 0.0) | |
| LOTERIA ILEGAL, CASA JUEGO Y PRÉSTAMO PRENDA ARTS 275 AL 283 | 13 ( 0.0) | |
| MALTRATO COMETIDO POR GARANTE ART. 403 BIS INC FINAL | 16 ( 0.0) | |
| MALTRATO CORPORAL A PERSONAS VULNERABLES ART 403 BIS INC 1° | 139 ( 0.0) | |
| MALTRATO DE OBRA A CARAB ART. 416 BIS COD DE JUST MILITAR | 1671 ( 0.3) | |
| MALTRATO DE OBRA FUNC PDI CON/SIN LESIONES 17 bis LOC PDI | 132 ( 0.0) | |
| MALTRATO HABITUAL (VIOLENCIA INTRAFAMILIAR) | 9439 ( 1.7) | |
| MALTRATO OBRA FISCAL, DEFENSOR EN DESEMP. DE FUNC. ART. 268 | 10 ( 0.0) | |
| MALTRATO OBRA GENDARME EN DESEMP. DE FUNC.ART 15 B,C DL 2859 | 141 ( 0.0) | |
| MALVERSACION DE CAUDALES PUBLICOS. ARTS 233, 234, 235 y 236 | 31 ( 0.0) | |
| MARCHARSE SITIO SUCESO S/AUXILIAR VÍCTIMA ART 195 INC 2 y 3 | 34 ( 0.0) | |
| MATAR A CARABINERO EN EJERCICIO DE SUS FUNCS. ART.416 C.J.M. | 10 ( 0.0) | |
| MICROTRÁFICO (TRÁFICO DE PEQUEÑAS CANTID. ART. 4 LEY 20000) | 5519 ( 1.0) | |
| MUERTES Y HALLAZGO DE CADÁVER | 1525 ( 0.3) | |
| NEGATIVA A EFECTUARSE EXAMEN. ART. 195 BIS LEY DE TRANSITO | 189 ( 0.0) | |
| NO DAR CUENTA DE ACCIDENTE DE TRÁNSITO ART. 96 D 1 LEY 18290 | 112 ( 0.0) | |
| NO DEFINIDO | 6 ( 0.0) | |
| NOMBRAMIENTOS ILEGALES | 1 ( 0.0) | |
| OBSTR INVESTIGACION POR FISCAL O ASISTENTE MP. ART.269 TER | 2 ( 0.0) | |
| OBSTRUCCIÓN A LA INVESTIGACIÓN. ART. 269 BIS. | 78 ( 0.0) | |
| OBSTRUCCIÓN A LA JUSTICIA CON OCASIÓN DE TRATAMIENTO DE ADN | 3 ( 0.0) | |
| OBTENCIÓN DE SERVICIOS SEXUALES DE MENORES. ART. 367 TER. | 16 ( 0.0) | |
| OBTENCIÓN DECLARACIONES FORZADAS ART.19 DL 2460 LEY ORG. INV | 1 ( 0.0) | |
| OBTENCION FRAUDULENTA DE CREDITOS. ART. 160. DFL 252 DE 1960 | 16 ( 0.0) | |
| OCULTACIÓN DE IDENT. CONTROL PREV. 496 Nº 5 Y 12 LEY 20.931 | 378 ( 0.1) | |
| OCULTACIÓN DE IDENTIDAD 496 Nº5 CÓDIGO PENAL | 866 ( 0.2) | |
| OCULTACIÓN IDENT. EN CONTROL INVESTIGATIVO 496 N°5 Y 85 CPP | 130 ( 0.0) | |
| OCULTACION O ENTR. DE INFO. FALSA A FNE. ART. 39 h) D.L. 211 | 1 ( 0.0) | |
| OCULTAMIENTO DE PLACA PATENTE (ART. 192 LETRA E) | 25 ( 0.0) | |
| OFENSAS AL PUDOR (495 Nº 5 Código Penal) | 418 ( 0.1) | |
| OMISIÓN DE DENUNCIAR POR FUNCIONARIO PÚB. LEY 20.000 ART. 13 | 3 ( 0.0) | |
| OPONERSE A ACCIÓN DE LA AUTORIDAD PÚBLICA O SUS AGENTES | 435 ( 0.1) | |
| OTORG. IRREGULAR DE DOCTOS (Art. 196 a Ley 18.290 Tráns) | 5 ( 0.0) | |
| OTRAS FALTAS CÓDIGO PENAL | 2872 ( 0.5) | |
| OTRAS FALTAS LEYES ESPECIALES | 4 ( 0.0) | |
| OTRAS FALTAS Y DELITOS DE LA LEY 19.733 | 2 ( 0.0) | |
| OTRAS INFRACCIÓN LEY 18.892 DE PESCA | 12 ( 0.0) | |
| OTRAS INFRACCIONES A LA LEY DEL BANCO CENTRAL | 19 ( 0.0) | |
| OTRAS INFRACCIONES A LA ORDENANZA ADUANAS. LEY 20.780 | 37 ( 0.0) | |
| OTRAS INFRACCIONES AL CÓDIGO JUSTICIA MILITAR | 39 ( 0.0) | |
| OTRAS INFRACCIONES CONTRA LA LEY DE ALCOHOLES | 1 ( 0.0) | |
| OTROS ABUSOS CONTRA PARTICULARES.ART. 256,257,258,259 | 17 ( 0.0) | |
| OTROS DE LOS CUASIDELITOS | 42 ( 0.0) | |
| OTROS DEL C/ ORDEN FAMILIA, MORALIDAD Pº, INTEGRIDAD SEXUAL | 69 ( 0.0) | |
| OTROS DELITOS EMPLEADOS PÚBLICOS EN DESEMPEÑO DE SUS CARGOS | 14 ( 0.0) | |
| OTROS DELITOS C/ FE PÚBLICA, FALSIFIC, FALSO TEST Y PERJURIO | 31 ( 0.0) | |
| OTROS DELITOS C/ ORDEN Y SEGURIDAD Pº COMETIDOS PARTICULARES | 25 ( 0.0) | |
| OTROS DELITOS CONTEMPLADOS EN LEY DE PROPIEDAD INTELECTUAL | 941 ( 0.2) | |
| OTROS DELITOS CONTRA LA LEY DEL TRÁNSITO | 628 ( 0.1) | |
| OTROS DELITOS CONTRA LA PROPIEDAD | 567 ( 0.1) | |
| OTROS DELITOS CONTRA LAS PERSONAS | 505 ( 0.1) | |
| OTROS DELITOS DE LA LEY 20.000 | 1478 ( 0.3) | |
| OTROS DELITOS DE LA LEY DE CONTROL DE ARMAS (LEY 17.798) | 152 ( 0.0) | |
| OTROS DELITOS LEY 19.327 SOBRE VIOLENCIA EN LOS ESTADIOS | 6 ( 0.0) | |
| OTROS DELITOS LEY DE CUENTAS CORRIENTES BANCARIAS Y CHEQUE | 75 ( 0.0) | |
| OTROS DELITOS LEY ORGÁNICA DE INVESTIGACIONES | 45 ( 0.0) | |
| OTROS DELITOS LY GRAL BANCOS.ART 110,141,142,157,159 DFL 252 | 23 ( 0.0) | |
| OTROS DELITOS QUE AFECTAN DºS GARANTIDOS POR LA CONSTITUCIÓN | 10 ( 0.0) | |
| OTROS ESTRAGOS | 43 ( 0.0) | |
| OTROS HECHOS | 8336 ( 1.5) | |
| OTROS LEY 18.314 | 1 ( 0.0) | |
| PARRICIDIO.ART. 390 inc.1° | 47 ( 0.0) | |
| PERRO POTENCIALMENTE PELIGROSO NO INSCRITO ART 16 LEY 21.020 | 3 ( 0.0) | |
| PORTAR ELEMENTOS CONOCIDAMENTE DESTINADOS A COMETER DELITO D | 1142 ( 0.2) | |
| PORTE ARMA GUERRA, QUÍMICA,BIOLÓGICA,NUCLEAR.ART. 14 INC. 2 | 1 ( 0.0) | |
| PORTE DE ARMA CORTANTE O PUNZANTE (288 bis) | 8443 ( 1.5) | |
| PORTE DE ARMA PROHIBIDA (ART. 14 INC. 1°) | 132 ( 0.0) | |
| PORTE ILEGAL DE ARMA DE FUEGO, MUNIC.Y OTR.SUJETAS A CONTROL | 1067 ( 0.2) | |
| POSESIÓN, TENENCIA ARMA GUERRA, QUÍM, BIOL, OTRA.ART 13 INC1 | 3 ( 0.0) | |
| POSESIÓN, TENENCIA, PORTE ARMAS ART 9 INC 1 LEY 17779 | 505 ( 0.1) | |
| POSESIÓN, TENENCIA, PORTE DE MUNIC Y SUST QUÍM ART 9 INC 2 | 134 ( 0.0) | |
| PRES. PERITOS, TEST O INTÉR.QUE FALTAREN A VERD.O DOC.FALSOS | 4 ( 0.0) | |
| PRESUNTA DESGRACIA | 19571 ( 3.4) | |
| PRESUNTA DESGRACIA INFANTIL | 1333 ( 0.2) | |
| PREVARICACIÓN DEL ABOGADO Y PROCURADOR ARTS. 231 y 232. | 10 ( 0.0) | |
| PREVARICACIÓN JUDICIAL Y ADMINISTRATIVA ART. 223 AL 229. | 2 ( 0.0) | |
| PRODUCCIÓN MATERIAL PORNOGRÁFICO UTILIZANDO MENOR.DE 18 AÑOS | 13 ( 0.0) | |
| PRODUCCIÓN Y TRÁFICO POR DESVÍO DE PRECURS. ART.2 LEY 20000 | 1 ( 0.0) | |
| PROMOVER O FACILITAR ENTRADA O SALIDA DEL PAIS PARA PROSTIT | 1 ( 0.0) | |
| PROMOVER O FACILITAR PROSTITUCIÓN DE MENORES . ART. 367. | 124 ( 0.0) | |
| QUEBRANTAMIENTO. ART. 90 | 540 ( 0.1) | |
| RECEPTACIÓN ADUANERA. INFRAC ORDZA ADUANAS ART 182 LEY 20780 | 10 ( 0.0) | |
| RECEPTACIÓN. ART. 456 BIS A | 9145 ( 1.6) | |
| RECONOCIMIENTO MALICIOSO DE POSESIÓN REG. ART.9 DEC LEY 2695 | 4 ( 0.0) | |
| REMISOS (RECLUTAMIENTO) DL 2306. Art. 73 | 96 ( 0.0) | |
| RIÑA PÚBLICA 496 Nº 10 CÓDIGO PENAL | 6497 ( 1.1) | |
| ROBO CON FUERZA DE CAJEROS AUTOMATICOS | 7 ( 0.0) | |
| ROBO CON HOMICIDIO | 35 ( 0.0) | |
| ROBO CON INTIMIDACIÓN . ART. 433, 436 INC 1º 438. | 13261 ( 2.3) | |
| ROBO CON LESIONES GRAVES GRAVISIMAS ART. 433 Nº 2 | 1 ( 0.0) | |
| ROBO CON RET. DE VICTIMAS O LESIONES GRAVES ART. 433 Nº 3 | 14 ( 0.0) | |
| ROBO CON RETENCIÓN DE VÍCTIMAS O CON LESIONES GRAVES | 49 ( 0.0) | |
| ROBO CON VIOLACIÓN. ART. 433 Nº1. | 79 ( 0.0) | |
| ROBO CON VIOLENCIA. ART.436 inc. 1º 433, 438, 439. | 10894 ( 1.9) | |
| ROBO DE VEHICULO MOTORIZADO ART. 443 INC. 2 | 2949 ( 0.5) | |
| ROBO EN BIENES NAC. DE USO PÚB. O SITIOS NO DESTINADOS A HAB | 6974 ( 1.2) | |
| ROBO EN LUGAR HABITADO O DESTINADO A LA HABITACIÓN. ART. 440 | 10883 ( 1.9) | |
| ROBO EN LUGAR NO HABITADO. ART. 442. | 9319 ( 1.6) | |
| ROBO O HURTO DE MATERIAL DE GUERRA | 2 ( 0.0) | |
| ROBO POR SORPRESA. ART. 436 INC. 2° | 6879 ( 1.2) | |
| ROTURA DE SELLOS.ARTS. 270 y 271. | 95 ( 0.0) | |
| RUIDOS MOLESTOS 495 Nº 1 CÓDIGO PENAL | 98 ( 0.0) | |
| SABOTAJE INFORMÁTICO. ARTS. 1 Y 3 LEY 19.223. | 64 ( 0.0) | |
| SECUESTRO CON HOMICIDIO, VIOLACIÓN O LES. ART.141 INC. FINAL | 33 ( 0.0) | |
| SECUESTRO CON LESIONES | 5 ( 0.0) | |
| SECUESTRO CON VIOLACIÓN | 3 ( 0.0) | |
| SECUESTRO. ART. 141 | 323 ( 0.1) | |
| SOBORNO DE FUNC PBCO EXTRANJ PERSONA NATURAL ART. 251 BIS | 2 ( 0.0) | |
| SODOMIA. ART. 365. | 4 ( 0.0) | |
| SUMINISTRO DE HIDROCARBUROS AROMÁTICOS A MEN.ART.5 LEY 20000 | 1 ( 0.0) | |
| SUMINISTRO INDEBIDO DE DROGAS. ART. 7 LEY Nº 20.000. | 2 ( 0.0) | |
| SUSTRACCIÓN DE MENORES. ART. 142 | 69 ( 0.0) | |
| TACHA FALSA DE FIRMA AUTÉNTICA. ART. 43. DFL 707 | 1 ( 0.0) | |
| TENENCIA DE ARMAS PROHIBIDAS ART. 13 | 50 ( 0.0) | |
| TOLERANCIA AL TRÁFICO O CONSUMO DE DROGAS. ART. 12 | 2 ( 0.0) | |
| TORTURAS COMETIDAS POR FUNCIONARIOS PÚBLICOS (150 A INC 1) | 148 ( 0.0) | |
| TORTURAS POR PARTICULARES AGENTES DEL ESTADO (150 A INC. 2) | 15 ( 0.0) | |
| TRÁFICO DE ARMAS (ART. 10) | 5 ( 0.0) | |
| TRAFICO DE ESPECIES VEGETALES. ART. 10 LEY 20.000 | 10 ( 0.0) | |
| TRAFICO DE MIGRANTES 411 BIS INCISO 1, 2 Y 3 | 1 ( 0.0) | |
| TRÁFICO DE RESIDUOS PELIGROSOS (LEY 20.920) | 6 ( 0.0) | |
| TRÁFICO ILÍCITO DE DROGAS ART. 3 LEY Nº 20.000. | 1874 ( 0.3) | |
| TRANSP.O DISTRIB.DE GAS E INST.CLAND. ART.53/54 DFL 323,1931 | 1 ( 0.0) | |
| TRANSPRTE DESECH. VERT. CLAND 192 BIS, INC. 6 LEY 20.879 | 1 ( 0.0) | |
| TRATA DE PERSONAS CON FINES DE PROSTITUCIÓN. ART. 367 BIS. | 12 ( 0.0) | |
| TRATA DE PERSONAS PARA LA EXPLOTACIÓN SEXUAL ART 411 QUATER | 1 ( 0.0) | |
| TRATA PARA TRABAJOS FORZADOS Y OTROS ART411 QUATER INC1° | 1 ( 0.0) | |
| TRATOS DEGRADANTES A PERSONAS VULNERABLES. ART. 403 TER. | 36 ( 0.0) | |
| ULTRAJE PÚBLICO A LAS BUENAS COSTUMBRES POR MED.COM.SOCIAL | 32 ( 0.0) | |
| ULTRAJE PÚBLICO A LAS BUENAS COSTUMBRES. ART. 373. | 125 ( 0.0) | |
| USO FRAUDULENTO DE TARJETAS O MEDIOS DE PAGO. LEY 20.009 | 1747 ( 0.3) | |
| USO ILICITO FUEGO ART 18,22,22BIS 22 terC18S 4363 LEYBOSQUES | 6 ( 0.0) | |
| USO UNIFORME O INSIGNIAS FFAA O CARABINEROS DE CHILE | 2 ( 0.0) | |
| USO,FACILITACIÓN O TRANSPORTE DE HILO CURADO. | 4 ( 0.0) | |
| USURA. ART. 472. | 2 ( 0.0) | |
| USURPACION DE AGUAS. ART. 459. | 13 ( 0.0) | |
| USURPACIÓN DE ATRIBUCIONES DE EMPLEADOS PÚB. Y JUDICIALES | 42 ( 0.0) | |
| USURPACIÓN DE ESTADO CIVIL. ART. 354. | 1 ( 0.0) | |
| USURPACIÓN DE NOMBRE. ART. 214. | 988 ( 0.2) | |
| USURPACIÓN DE PROPIEDAD, DESCUBRIMIENTO O PROD. ART.158 Nº5. | 196 ( 0.0) | |
| USURPACIÓN NO VIOLENTA. ART. 458. | 287 ( 0.1) | |
| USURPACIÓN VIOLENTA. ART. 457 INCISO PRIMERO. | 52 ( 0.0) | |
| VENTA ILICITA OBRAS PROTEGIDAS LEY PROP INTELECT ART. 81 | 178 ( 0.0) | |
| VIOLACIÓN | 4 ( 0.0) | |
| VIOLACIÓN CON HOMICIDIO O FEMICIDIO ART. 372 BIS. | 1 ( 0.0) | |
| VIOLACIÓN DE MAYOR DE 14 AÑOS. ART. 361. | 1665 ( 0.3) | |
| VIOLACIÓN DE MENOR DE 14 AÑOS. ART. 362. | 441 ( 0.1) | |
| VIOLACIÓN DE MORADA. ART. 144 | 5080 ( 0.9) | |
| VIOLACIÓN DE SECRETOS. ART. 246. | 1 ( 0.0) | |
| VIOLENCIA EN LOS ESTADIOS ART 6,6A-B-D-E-F-G-H LEY 19.327 | 61 ( 0.0) | |
| familia_delito (%) | CUASIDELITOS | 3837 ( 0.7) |
| DELITOS CONTRA LA FE PÚBLICA | 15985 ( 2.8) | |
| DELITOS CONTRA LA LIBERTAD E INTIMIDAD DE LAS PERSONAS | 98960 ( 17.4) | |
| DELITOS CONTRA LEYES DE PROPIEDAD INTELECTUAL E INDUSTRIAL | 1473 ( 0.3) | |
| DELITOS DE JUSTICIA MILITAR | 47 ( 0.0) | |
| DELITOS DE LEYES ESPECIALES | 13230 ( 2.3) | |
| DELITOS DE TORTURA, MALOS TRATOS, GENOCIDIO Y LESA HUMANIDAD | 453 ( 0.1) | |
| DELITOS ECONÓMICOS Y TRIBUTARIOS | 14271 ( 2.5) | |
| DELITOS FUNCIONARIOS | 738 ( 0.1) | |
| DELITOS LEY DE DROGAS | 9576 ( 1.7) | |
| DELITOS LEY DE TRÁNSITO | 16101 ( 2.8) | |
| DELITOS SEXUALES | 5860 ( 1.0) | |
| FALTAS | 80854 ( 14.2) | |
| HECHOS DE RELEVANCIA CRIMINAL | 32265 ( 5.7) | |
| HOMICIDIOS | 1107 ( 0.2) | |
| HURTOS | 52088 ( 9.2) | |
| LESIONES | 118835 ( 20.9) | |
| OTROS DELITOS | 12596 ( 2.2) | |
| OTROS DELITOS CONTRA LA PROPIEDAD | 28677 ( 5.0) | |
| ROBOS | 31212 ( 5.5) | |
| ROBOS NO VIOLENTOS | 30132 ( 5.3) | |
| relacion_vifsaf (%) | NO | 452460 ( 79.6) |
| SI | 115837 ( 20.4) | |
| gls_parentesco (%) | ABUELO(A) | 747 ( 0.1) |
| ADOPTADO | 41 ( 0.0) | |
| ADOPTANTE | 5 ( 0.0) | |
| CONVIVIENTE | 30461 ( 5.4) | |
| CÓNYUGE | 15104 ( 2.7) | |
| CUÑADO(A) | 1701 ( 0.3) | |
| EX CONVIVIENTE | 21379 ( 3.8) | |
| EX CONYUGE | 1417 ( 0.2) | |
| HERMANO(A) | 9836 ( 1.7) | |
| HIJO(A) | 9131 ( 1.6) | |
| MADRASTRA | 103 ( 0.0) | |
| MADRE | 8412 ( 1.5) | |
| MADRE HIJO COMUN | 3885 ( 0.7) | |
| MENOR DE EDAD (bajo cuidado) | 44 ( 0.0) | |
| MINUSVALIDO (bajo cuidado) | 3 ( 0.0) | |
| NIETO | 352 ( 0.1) | |
| No Definido | 118 ( 0.0) | |
| NUERA | 259 ( 0.0) | |
| PADRASTRO | 762 ( 0.1) | |
| PADRE | 4264 ( 0.8) | |
| PADRE HIJO COMUN | 3584 ( 0.6) | |
| SOBRINO(A) | 1451 ( 0.3) | |
| SUEGRO(A) | 866 ( 0.2) | |
| TIO(A) | 1509 ( 0.3) | |
| YERNO | 403 ( 0.1) | |
| 452460 ( 79.6) | ||
| gls_mottermino (%) | Acoge req. proced monit y proposic multa | 57060 ( 10.0) |
| Acuerdo Reparatorio (Art. 241 y 242) | 12071 ( 2.1) | |
| Agrupado | 28281 ( 5.0) | |
| Anulación de ingreso error de digitación | 1575 ( 0.3) | |
| Anulación ingreso por delito acción priv | 33 ( 0.0) | |
| Archivo Provisional | 149674 ( 26.3) | |
| Decisión de no perseverar en el proced | 33205 ( 5.8) | |
| Incomp materias correspond sistema antig | 87 ( 0.0) | |
| Incomp tratarse materias conoc otro trib | 2346 ( 0.4) | |
| Negativa investig caso autodenuncia 179 | 1 ( 0.0) | |
| No Inicio Investigación | 42251 ( 7.4) | |
| Otras causales de término | 1255 ( 0.2) | |
| Principio de Oportunidad | 45202 ( 8.0) | |
| Rechazo querella de capítulos (art. 429) | 3 ( 0.0) | |
| Rechazo req. Med. Seg. Opuest qte 462 | 2 ( 0.0) | |
| S Def (no subs oport vicios form art270) | 70 ( 0.0) | |
| S Definit (vencim plazo suspens art 240) | 44628 ( 7.9) | |
| S. Definit (en casos del art. 465) | 3 ( 0.0) | |
| S. Definit (exc previo y espec pron 271) | 22 ( 0.0) | |
| S. Definit (susp imposic de la cond 398) | 44 ( 0.0) | |
| Sentencia definitiva absolutoria | 11852 ( 2.1) | |
| Sentencia definitiva condenatoria | 100130 ( 17.6) | |
| Sobres Definit (en casos del art 247) | 12 ( 0.0) | |
| Sobres Definitivo (causales del art 250) | 28134 ( 5.0) | |
| Término requerimiento internacional | 3 ( 0.0) | |
| 10353 ( 1.8) | ||
| gls_motsuspension (%) | ACUERDO REPARATORIO | 15456 ( 2.7) |
| SOBRESEIMIENTO TEMPORAL | 2275 ( 0.4) | |
| SUSPENSION CONDICIONAL DEL PROCEDIMIENTO | 55631 ( 9.8) | |
| SUSPENSION PROCED. ART. 10 | 10 ( 0.0) | |
| SUSPENSION PROCED. ART. 458 | 1719 ( 0.3) | |
| 493206 ( 86.8) | ||
| gls_proctermino (%) | ABREVIADO | 21460 ( 3.8) |
| MONITORIO | 57113 ( 10.0) | |
| ORDINARIO | 106814 ( 18.8) | |
| RECLAMO | 1 ( 0.0) | |
| REQUERIMIENTO INTERNACIONAL | 3 ( 0.0) | |
| SIMPLIFICADO | 102582 ( 18.1) | |
| SIN PROCEDIMIENTO | 280324 ( 49.3) | |
| gls_tipo_imputado (%) | DENUNCIADO | 28397 ( 5.0) |
| IMPUTADO | 538987 ( 94.8) | |
| INVESTIGADO | 27 ( 0.0) | |
| QUERELLADO | 562 ( 0.1) | |
| SOSPECHOSO | 234 ( 0.0) | |
| TESTIGO | 90 ( 0.0) | |
| iddelito (mean (SD)) | 13437430.95 (4192444.01) | |
| lugar_ocurrencia (%) | BIENES NACIONALES USO PUBLICO | 221138 ( 38.9) |
| ESTABLECIMIENTO COMERCIAL | 91267 ( 16.1) | |
| ESTABLECIMIENTO DE DIVERSION | 1853 ( 0.3) | |
| ESTABLECIMIENTO EDUCACIONAL | 3503 ( 0.6) | |
| ESTABLECIMIENTO PRODUCTIVO | 8204 ( 1.4) | |
| LUGAR HABITADO O DESTINADO A LA HABITACION Y SUS DEPENDENCIAS | 213180 ( 37.5) | |
| ORGANIZACION DE SEGURIDAD / JUSTICIA | 12246 ( 2.2) | |
| ORGANIZACION PUBLICA Y/O SOCIAL | 7376 ( 1.3) | |
| OTROS LUGARES NO CLASIFICADOS | 5664 ( 1.0) | |
| 3866 ( 0.7) | ||
| gls_sitiosuceso (%) | ANEXOS VIVIENDA | 1920 ( 0.3) |
| AUTOSERVICIO | 36710 ( 6.5) | |
| BOMBEROS | 21 ( 0.0) | |
| CAJERO AUTOMATICO | 536 ( 0.1) | |
| CARRETERAS Y CAMINOS | 12485 ( 2.2) | |
| CEMENTERIOS | 226 ( 0.0) | |
| CENTRALES DE APUESTAS | 72 ( 0.0) | |
| CENTRO ESPECTACULOS | 291 ( 0.1) | |
| CENTRO RECREATIVO | 526 ( 0.1) | |
| CENTRO SOCIAL | 873 ( 0.2) | |
| CENTROS CULTURALES | 66 ( 0.0) | |
| CENTROS DE ASISTENCIA MEDICA | 3626 ( 0.6) | |
| CENTROS DE CULTO | 241 ( 0.0) | |
| CENTROS DE JUEGOS | 200 ( 0.0) | |
| CERRO | 135 ( 0.0) | |
| COMERCIO MENOR | 8248 ( 1.5) | |
| CUERPO DE AGUA | 27 ( 0.0) | |
| CURSO DE AGUA | 25 ( 0.0) | |
| DEFENSORIA | 10 ( 0.0) | |
| DISCOTEQUE / PUB | 1175 ( 0.2) | |
| EMPRESA / COMPAÑIA | 4178 ( 0.7) | |
| ESCOLAR | 2900 ( 0.5) | |
| ESTABLECIMIENTO FINANCIERO | 1997 ( 0.4) | |
| ESTABLECIMIENTOS DE ALIMENTACION | 4109 ( 0.7) | |
| ESTACIONAMIENTO ABIERTO | 461 ( 0.1) | |
| ESTACIONAMIENTO CERRADO | 282 ( 0.0) | |
| EXPENDIO DE BEBIDAS ALCOHOLICAS | 462 ( 0.1) | |
| FERIAS LIBRES | 505 ( 0.1) | |
| FISCALIA | 66 ( 0.0) | |
| INDUSTRIA | 3274 ( 0.6) | |
| MAR | 115 ( 0.0) | |
| MINAS Y PIQUES MINEROS | 38 ( 0.0) | |
| PARQUE / PLAZA | 1220 ( 0.2) | |
| PLAYA | 366 ( 0.1) | |
| PRE-ESCOLAR | 265 ( 0.0) | |
| PREDIO RURAL | 3402 ( 0.6) | |
| PROSTIBULOS | 114 ( 0.0) | |
| PUENTES | 30 ( 0.0) | |
| RECINTOS DE GENDARMERIA | 8527 ( 1.5) | |
| RECINTOS FFAA | 224 ( 0.0) | |
| RECINTOS POLICIALES | 2187 ( 0.4) | |
| SERVICENTRO | 2527 ( 0.4) | |
| SERVICIOS PUBLICOS | 2023 ( 0.4) | |
| SITIO ERIAZO PARTICULAR | 1865 ( 0.3) | |
| SITIO ERIAZO PUBLICO | 273 ( 0.0) | |
| SUPERIOR | 329 ( 0.1) | |
| TALLER | 694 ( 0.1) | |
| TERMINALES DE TRANSPORTES | 2101 ( 0.4) | |
| TIENDA COMERCIAL | 34455 ( 6.1) | |
| TRIBUNALES | 1206 ( 0.2) | |
| VEHICULO EN VIA PUBLICA | 6483 ( 1.1) | |
| VIA PUBLICA URBANA | 198343 ( 34.9) | |
| VIVIENDA | 209068 ( 36.8) | |
| VIVIENDAS TRANSITORIAS | 1992 ( 0.4) | |
| 4803 ( 0.8) | ||
| gls_comuna (%) | AISEN | 1314 ( 0.2) |
| ALGARROBO | 443 ( 0.1) | |
| ALHUE | 74 ( 0.0) | |
| ALTO BIO BIO | 30 ( 0.0) | |
| ALTO DEL CARMEN | 47 ( 0.0) | |
| ALTO HOSPICIO | 5728 ( 1.0) | |
| ANCUD | 474 ( 0.1) | |
| ANDACOLLO | 475 ( 0.1) | |
| ANGOL | 1187 ( 0.2) | |
| ANTARTICA | 2 ( 0.0) | |
| ANTOFAGASTA | 14248 ( 2.5) | |
| ANTUCO | 29 ( 0.0) | |
| ARAUCO | 1285 ( 0.2) | |
| ARICA | 13870 ( 2.4) | |
| BUIN | 2508 ( 0.4) | |
| BULNES | 142 ( 0.0) | |
| CABILDO | 229 ( 0.0) | |
| CABRERO | 291 ( 0.1) | |
| CALAMA | 8473 ( 1.5) | |
| CALBUCO | 674 ( 0.1) | |
| CALDERA | 2344 ( 0.4) | |
| CALERA DE TANGO | 791 ( 0.1) | |
| CALLE LARGA | 135 ( 0.0) | |
| CAMARONES | 30 ( 0.0) | |
| CAMINA | 9 ( 0.0) | |
| CANELA | 47 ( 0.0) | |
| CAÑETE | 1126 ( 0.2) | |
| CARAHUE | 375 ( 0.1) | |
| CARTAGENA | 1394 ( 0.2) | |
| CASABLANCA | 1432 ( 0.3) | |
| CASTRO | 1499 ( 0.3) | |
| CATEMU | 342 ( 0.1) | |
| CAUQUENES | 1171 ( 0.2) | |
| CERRILLOS | 3484 ( 0.6) | |
| CERRO NAVIA | 4628 ( 0.8) | |
| CHAITEN | 37 ( 0.0) | |
| CHANCO | 74 ( 0.0) | |
| CHAÑARAL | 2238 ( 0.4) | |
| CHEPICA | 283 ( 0.0) | |
| CHIGUAYANTE | 1296 ( 0.2) | |
| CHILE CHICO | 426 ( 0.1) | |
| CHILLAN | 3764 ( 0.7) | |
| CHILLAN VIEJO | 302 ( 0.1) | |
| CHIMBARONGO | 623 ( 0.1) | |
| CHOL CHOL | 70 ( 0.0) | |
| CHONCHI | 152 ( 0.0) | |
| CISNES | 460 ( 0.1) | |
| COBQUECURA | 27 ( 0.0) | |
| COCHAMO | 5 ( 0.0) | |
| COCHRANE | 288 ( 0.1) | |
| CODEGUA | 226 ( 0.0) | |
| COELEMU | 157 ( 0.0) | |
| COIHUECO | 52 ( 0.0) | |
| COINCO | 63 ( 0.0) | |
| COLBUN | 102 ( 0.0) | |
| COLCHANE | 9 ( 0.0) | |
| COLINA | 7869 ( 1.4) | |
| COLLIPULLI | 976 ( 0.2) | |
| COLTAUCO | 209 ( 0.0) | |
| COMBARBALA | 124 ( 0.0) | |
| CONCEPCION | 7577 ( 1.3) | |
| CONCHALI | 4937 ( 0.9) | |
| CONCON | 1086 ( 0.2) | |
| CONSTITUCION | 1449 ( 0.3) | |
| CONTULMO | 39 ( 0.0) | |
| COPIAPO | 7567 ( 1.3) | |
| COQUIMBO | 7279 ( 1.3) | |
| CORONEL | 4175 ( 0.7) | |
| CORRAL | 26 ( 0.0) | |
| COYHAIQUE | 2693 ( 0.5) | |
| CUNCO | 80 ( 0.0) | |
| CURACAUTIN | 79 ( 0.0) | |
| CURACAVI | 1618 ( 0.3) | |
| CURACO DE VELEZ | 8 ( 0.0) | |
| CURANILAHUE | 1169 ( 0.2) | |
| CURARREHUE | 105 ( 0.0) | |
| CUREPTO | 38 ( 0.0) | |
| CURICO | 8686 ( 1.5) | |
| DALCAHUE | 191 ( 0.0) | |
| DIEGO DE ALMAGRO | 1030 ( 0.2) | |
| DOQIHUE | 255 ( 0.0) | |
| EL BOSQUE | 6006 ( 1.1) | |
| EL CARMEN | 40 ( 0.0) | |
| EL MONTE | 1666 ( 0.3) | |
| EL QUISCO | 755 ( 0.1) | |
| EL TABO | 563 ( 0.1) | |
| EMPEDRADO | 19 ( 0.0) | |
| ERCILLA | 43 ( 0.0) | |
| ESTACION CENTRAL | 7182 ( 1.3) | |
| FLORIDA | 80 ( 0.0) | |
| FREIRE | 113 ( 0.0) | |
| FREIRINA | 410 ( 0.1) | |
| FRESIA | 53 ( 0.0) | |
| FRUTILLAR | 329 ( 0.1) | |
| FUTALEUFU | 30 ( 0.0) | |
| FUTRONO | 59 ( 0.0) | |
| GALVARINO | 35 ( 0.0) | |
| GENERAL LAGOS | 1 ( 0.0) | |
| GORBEA | 72 ( 0.0) | |
| GRANEROS | 1809 ( 0.3) | |
| GUAITECAS | 37 ( 0.0) | |
| HIJUELAS | 251 ( 0.0) | |
| HUALAIHUE | 66 ( 0.0) | |
| HUALAQE | 210 ( 0.0) | |
| HUALPEN | 1914 ( 0.3) | |
| HUALQUI | 198 ( 0.0) | |
| HUARA | 101 ( 0.0) | |
| HUASCO | 373 ( 0.1) | |
| HUECHURABA | 2971 ( 0.5) | |
| ILLAPEL | 268 ( 0.0) | |
| INDEPENDENCIA | 3010 ( 0.5) | |
| IQUIQUE | 10697 ( 1.9) | |
| ISLA DE MAIPO | 1135 ( 0.2) | |
| ISLA DE PASCUA | 1893 ( 0.3) | |
| JUAN FERNANDEZ | 18 ( 0.0) | |
| LA CALERA | 1495 ( 0.3) | |
| LA CISTERNA | 3707 ( 0.7) | |
| LA CRUZ | 239 ( 0.0) | |
| LA ESTRELLA | 58 ( 0.0) | |
| LA FLORIDA | 12025 ( 2.1) | |
| LA GRANJA | 4920 ( 0.9) | |
| LA HIGUERA | 104 ( 0.0) | |
| LA LIGUA | 736 ( 0.1) | |
| LA PINTANA | 9202 ( 1.6) | |
| LA REINA | 2537 ( 0.4) | |
| LA SERENA | 8020 ( 1.4) | |
| LA UNION | 715 ( 0.1) | |
| LAGO RANCO | 51 ( 0.0) | |
| LAGO VERDE | 27 ( 0.0) | |
| LAGUNA BLANCA | 2 ( 0.0) | |
| LAJA | 418 ( 0.1) | |
| LAMPA | 3214 ( 0.6) | |
| LANCO | 96 ( 0.0) | |
| LAS CABRAS | 435 ( 0.1) | |
| LAS CONDES | 3949 ( 0.7) | |
| LAUTARO | 245 ( 0.0) | |
| LEBU | 636 ( 0.1) | |
| LICANTEN | 298 ( 0.1) | |
| LIMACHE | 878 ( 0.2) | |
| LINARES | 2313 ( 0.4) | |
| LITUECHE | 121 ( 0.0) | |
| LLAILLAY | 705 ( 0.1) | |
| LLANQUIHUE | 238 ( 0.0) | |
| LO BARNECHEA | 3028 ( 0.5) | |
| LO ESPEJO | 4461 ( 0.8) | |
| LO PRADO | 5157 ( 0.9) | |
| LOLOL | 35 ( 0.0) | |
| LONCOCHE | 226 ( 0.0) | |
| LONGAVI | 175 ( 0.0) | |
| LONQUIMAY | 31 ( 0.0) | |
| LOS ALAMOS | 234 ( 0.0) | |
| LOS ANDES | 2407 ( 0.4) | |
| LOS ANGELES | 4698 ( 0.8) | |
| LOS LAGOS | 94 ( 0.0) | |
| LOS MUERMOS | 61 ( 0.0) | |
| LOS SAUCES | 332 ( 0.1) | |
| LOS VILOS | 1111 ( 0.2) | |
| LOTA | 1688 ( 0.3) | |
| LUMACO | 77 ( 0.0) | |
| MACHALI | 920 ( 0.2) | |
| MACUL | 4537 ( 0.8) | |
| MAFIL | 34 ( 0.0) | |
| MAIPU | 9902 ( 1.7) | |
| MALLOA | 155 ( 0.0) | |
| MARCHIHUE | 109 ( 0.0) | |
| MARIA ELENA | 174 ( 0.0) | |
| MARIA PINTO | 562 ( 0.1) | |
| MARIQUINA | 239 ( 0.0) | |
| MAULE | 589 ( 0.1) | |
| MAULLIN | 69 ( 0.0) | |
| MEJILLONES | 603 ( 0.1) | |
| MELIPEUCO | 57 ( 0.0) | |
| MELIPILLA | 8236 ( 1.4) | |
| MOLINA | 1383 ( 0.2) | |
| MONTE PATRIA | 468 ( 0.1) | |
| MOSTAZAL | 835 ( 0.1) | |
| MULCHEN | 499 ( 0.1) | |
| NACIMIENTO | 417 ( 0.1) | |
| NANCAGUA | 468 ( 0.1) | |
| NATALES | 732 ( 0.1) | |
| NAVARINO | 26 ( 0.0) | |
| NAVIDAD | 67 ( 0.0) | |
| NEGRETE | 101 ( 0.0) | |
| NINHUE | 12 ( 0.0) | |
| No Definido | 4 ( 0.0) | |
| NOGALES | 325 ( 0.1) | |
| NUEVA IMPERIAL | 730 ( 0.1) | |
| ÑIQUEN | 24 ( 0.0) | |
| ÑUÑOA | 4617 ( 0.8) | |
| OHIGGINS | 31 ( 0.0) | |
| OLIVAR | 219 ( 0.0) | |
| OLLAGUE | 3 ( 0.0) | |
| OLMUE | 85 ( 0.0) | |
| OSORNO | 5155 ( 0.9) | |
| OVALLE | 3591 ( 0.6) | |
| PADRE DE LAS CASAS | 763 ( 0.1) | |
| PADRE HURTADO | 1763 ( 0.3) | |
| PAIHUANO | 110 ( 0.0) | |
| PAILLACO | 200 ( 0.0) | |
| PAINE | 1448 ( 0.3) | |
| PALENA | 20 ( 0.0) | |
| PALMILLA | 164 ( 0.0) | |
| PANGUIPULLI | 435 ( 0.1) | |
| PANQUEHUE | 130 ( 0.0) | |
| PAPUDO | 44 ( 0.0) | |
| PAREDONES | 38 ( 0.0) | |
| PARRAL | 899 ( 0.2) | |
| PEDRO AGUIRRE CERDA | 3995 ( 0.7) | |
| PELARCO | 150 ( 0.0) | |
| PELLUHUE | 306 ( 0.1) | |
| PEMUCO | 22 ( 0.0) | |
| PENCAHUE | 67 ( 0.0) | |
| PENCO | 1559 ( 0.3) | |
| PEÑAFLOR | 3416 ( 0.6) | |
| PEÑALOLEN | 8472 ( 1.5) | |
| PERALILLO | 264 ( 0.0) | |
| PERQUENCO | 25 ( 0.0) | |
| PETORCA | 104 ( 0.0) | |
| PEUMO | 411 ( 0.1) | |
| PICA | 450 ( 0.1) | |
| PICHIDEGUA | 184 ( 0.0) | |
| PICHILEMU | 1593 ( 0.3) | |
| PINTO | 97 ( 0.0) | |
| PIRQUE | 532 ( 0.1) | |
| PITRUFQUEN | 154 ( 0.0) | |
| PLACILLA | 121 ( 0.0) | |
| PORTEZUELO | 14 ( 0.0) | |
| PORVENIR | 90 ( 0.0) | |
| POZO ALMONTE | 1010 ( 0.2) | |
| PRIMAVERA | 8 ( 0.0) | |
| PROVIDENCIA | 4025 ( 0.7) | |
| PUCHUNCAVI | 720 ( 0.1) | |
| PUCON | 1542 ( 0.3) | |
| PUDAHUEL | 10528 ( 1.9) | |
| PUENTE ALTO | 16927 ( 3.0) | |
| PUERTO MONTT | 6607 ( 1.2) | |
| PUERTO OCTAY | 123 ( 0.0) | |
| PUERTO VARAS | 1152 ( 0.2) | |
| PUMANQUE | 8 ( 0.0) | |
| PUNITAQUI | 151 ( 0.0) | |
| PUNTA ARENAS | 4333 ( 0.8) | |
| PUQUELDON | 13 ( 0.0) | |
| PUREN | 431 ( 0.1) | |
| PURRANQUE | 604 ( 0.1) | |
| PUTAENDO | 186 ( 0.0) | |
| PUTRE | 22 ( 0.0) | |
| PUYEHUE | 44 ( 0.0) | |
| QUEILEN | 17 ( 0.0) | |
| QUELLON | 1232 ( 0.2) | |
| QUEMCHI | 70 ( 0.0) | |
| QUILACO | 53 ( 0.0) | |
| QUILICURA | 6867 ( 1.2) | |
| QUILLECO | 32 ( 0.0) | |
| QUILLON | 232 ( 0.0) | |
| QUILLOTA | 4114 ( 0.7) | |
| QUILPUE | 2634 ( 0.5) | |
| QUINCHAO | 49 ( 0.0) | |
| QUINTA DE TILCOCO | 188 ( 0.0) | |
| QUINTA NORMAL | 3796 ( 0.7) | |
| QUINTERO | 2020 ( 0.4) | |
| QUIRIHUE | 132 ( 0.0) | |
| RANCAGUA | 11887 ( 2.1) | |
| RANQUIL | 33 ( 0.0) | |
| RAUCO | 92 ( 0.0) | |
| RECOLETA | 6252 ( 1.1) | |
| RENAICO | 396 ( 0.1) | |
| RENCA | 5049 ( 0.9) | |
| RENGO | 2138 ( 0.4) | |
| REQUINOA | 357 ( 0.1) | |
| RETIRO | 67 ( 0.0) | |
| RINCONADA | 166 ( 0.0) | |
| RIO BUENO | 646 ( 0.1) | |
| RIO CLARO | 88 ( 0.0) | |
| RIO HURTADO | 15 ( 0.0) | |
| RIO IBAQEZ | 23 ( 0.0) | |
| RIO NEGRO | 254 ( 0.0) | |
| RIO VERDE | 3 ( 0.0) | |
| ROMERAL | 351 ( 0.1) | |
| SAAVEDRA | 79 ( 0.0) | |
| SAGRADA FAMILIA | 368 ( 0.1) | |
| SALAMANCA | 185 ( 0.0) | |
| SAN ANTONIO | 3880 ( 0.7) | |
| SAN BERNARDO | 11907 ( 2.1) | |
| SAN CARLOS | 380 ( 0.1) | |
| SAN CLEMENTE | 749 ( 0.1) | |
| SAN ESTEBAN | 295 ( 0.1) | |
| SAN FABIAN | 20 ( 0.0) | |
| SAN FELIPE | 2489 ( 0.4) | |
| SAN FERNANDO | 4961 ( 0.9) | |
| SAN IGNACIO | 75 ( 0.0) | |
| SAN JAVIER | 1165 ( 0.2) | |
| SAN JOAQUIN | 3834 ( 0.7) | |
| SAN JOSE DE MAIPO | 710 ( 0.1) | |
| SAN JUAN DE LA COSTA | 60 ( 0.0) | |
| SAN MIGUEL | 4601 ( 0.8) | |
| SAN NICOLAS | 21 ( 0.0) | |
| SAN PABLO | 70 ( 0.0) | |
| SAN PEDRO | 143 ( 0.0) | |
| SAN PEDRO DE ATACAMA | 477 ( 0.1) | |
| SAN PEDRO DE LA PAZ | 2249 ( 0.4) | |
| SAN RAFAEL | 98 ( 0.0) | |
| SAN RAMON | 4878 ( 0.9) | |
| SAN ROSENDO | 36 ( 0.0) | |
| SAN VICENTE | 2026 ( 0.4) | |
| SANTA BARBARA | 525 ( 0.1) | |
| SANTA CRUZ | 2260 ( 0.4) | |
| SANTA JUANA | 326 ( 0.1) | |
| SANTA MARIA | 227 ( 0.0) | |
| SANTIAGO | 17075 ( 3.0) | |
| SANTO DOMINGO | 118 ( 0.0) | |
| SIERRA GORDA | 42 ( 0.0) | |
| TALAGANTE | 4381 ( 0.8) | |
| TALCA | 8231 ( 1.4) | |
| TALCAHUANO | 4009 ( 0.7) | |
| TALTAL | 1212 ( 0.2) | |
| TEMUCO | 4709 ( 0.8) | |
| TENO | 343 ( 0.1) | |
| TEODORO SCHMIDT | 73 ( 0.0) | |
| TIERRA AMARILLA | 476 ( 0.1) | |
| TIL TIL | 790 ( 0.1) | |
| TINAUKEL | 3 ( 0.0) | |
| TIRUA | 62 ( 0.0) | |
| TOCOPILLA | 2060 ( 0.4) | |
| TOLTEN | 39 ( 0.0) | |
| TOME | 1473 ( 0.3) | |
| TORRES DEL PAINE | 2 ( 0.0) | |
| TORTEL | 65 ( 0.0) | |
| TRAIGUEN | 198 ( 0.0) | |
| TREGUACO | 32 ( 0.0) | |
| TUCAPEL | 86 ( 0.0) | |
| VALDIVIA | 5850 ( 1.0) | |
| VALLENAR | 3206 ( 0.6) | |
| VALPARAISO | 10402 ( 1.8) | |
| VICHUQUEN | 38 ( 0.0) | |
| VICTORIA | 1205 ( 0.2) | |
| VICUÑA | 1116 ( 0.2) | |
| VILCUN | 115 ( 0.0) | |
| VILLA ALEGRE | 429 ( 0.1) | |
| VILLA ALEMANA | 2256 ( 0.4) | |
| VILLARRICA | 1011 ( 0.2) | |
| VIQA DEL MAR | 10056 ( 1.8) | |
| VITACURA | 829 ( 0.1) | |
| YERBAS BUENAS | 110 ( 0.0) | |
| YUMBEL | 280 ( 0.0) | |
| YUNGAY | 134 ( 0.0) | |
| ZAPALLAR | 79 ( 0.0) | |
| 1048 ( 0.2) | ||
| region_delito (%) | RM Centro Norte | 18 ( 0.0) |
| RM Occidente | 13 ( 0.0) | |
| RM Oriente | 9 ( 0.0) | |
| RM Sur | 99 ( 0.0) | |
| 568158 (100.0) | ||
| medidas_155 (%) | SI | 57377 ( 10.1) |
| 510920 ( 89.9) | ||
| medidas_pp (%) | SI | 22296 ( 3.9) |
| 546001 ( 96.1) | ||
| medidas_ip (%) | SI | 733 ( 0.1) |
| 567564 ( 99.9) | ||
| marca_suspension_43 (%) | NO | 67195 ( 11.8) |
| SI | 2341 ( 0.4) | |
| 498761 ( 87.8) | ||
| marca_pena_44 (%) | SI | 69536 ( 12.2) |
| 498761 ( 87.8) | ||
| marca_multa_45 (%) | NO | 35220 ( 6.2) |
| SI | 34316 ( 6.0) | |
| 498761 ( 87.8) | ||
| medida_alternativa_46 (%) | Libertad Vigilada | 3219 ( 0.6) |
| Otra | 4988 ( 0.9) | |
| Reclusión Nocturna | 14058 ( 2.5) | |
| Remisión Condicional | 18345 ( 3.2) | |
| 527687 ( 92.9) | ||
| clasificacion_pena_47 (%) | Presidio Mayor grado máximo | 88 ( 0.0) |
| Presidio Mayor grado medio | 468 ( 0.1) | |
| Presidio Mayor grado mínimo | 1927 ( 0.3) | |
| Presidio Menor grado máximo | 4990 ( 0.9) | |
| Presidio Menor grado medio | 8798 ( 1.5) | |
| Presidio Menor grado mínimo | 29560 ( 5.2) | |
| Presidio Perpetuo calificado | 1 ( 0.0) | |
| Presidio Perpetuo simple | 15 ( 0.0) | |
| Prisión | 23689 ( 4.2) | |
| 498761 ( 87.8) | ||
| marca_suspension_51 (%) | NO | 74 ( 0.0) |
| SI | 3 ( 0.0) | |
| 568220 (100.0) | ||
| marca_pena_52 (%) | SI | 77 ( 0.0) |
| 568220 (100.0) | ||
| marca_multa_53 (%) | NO | 46 ( 0.0) |
| SI | 31 ( 0.0) | |
| 568220 (100.0) | ||
| medida_alternativa_54 (%) | Libertad Vigilada | 8 ( 0.0) |
| Otra | 5 ( 0.0) | |
| Reclusión Nocturna | 17 ( 0.0) | |
| Remisión Condicional | 15 ( 0.0) | |
| 568252 (100.0) | ||
| clasificacion_pena_55 (%) | Presidio Mayor grado medio | 3 ( 0.0) |
| Presidio Mayor grado mínimo | 5 ( 0.0) | |
| Presidio Menor grado máximo | 12 ( 0.0) | |
| Presidio Menor grado medio | 9 ( 0.0) | |
| Presidio Menor grado mínimo | 21 ( 0.0) | |
| Prisión | 27 ( 0.0) | |
| 568220 (100.0) | ||
| Note. Continuous variables are presented as Medians and Percentiles 25 and 75 were shown; | ||
| Categorical variables are presented as number (%) |
tab2 <- CreateTableOne(vars = myVars, data = base_fiscalia[,myVars], factorVars = catVars,
smd=T, strata="encontrado_como_imputado", includeNA=T,test=T)## Warning in ModuleReturnVarsExist(vars, data): These variables only have NA/
## NaN: tramos_condena_48 clasificacion_penarpa_1_49 clasificacion_penarpa_2_50
## tramos_condena_56 clasificacion_penarpa_1_57 clasificacion_penarpa_2_58 Dropped## Warning in ModuleReturnVarsExist(factorVars, data): These variables only have
## NA/NaN: tramos_condena_48 clasificacion_penarpa_1_49 clasificacion_penarpa_2_50
## tramos_condena_56 clasificacion_penarpa_1_57 clasificacion_penarpa_2_58 Dropped## Warning in CreateTableOne(vars = myVars, data = base_fiscalia[, myVars], : Dropping variable(s) fec_comision termino_relacion fec_cbiorelacion fec_comision_simple fec_cbiorelacion_simple termino_relacion_simple due to unsupported class.kableone(as.data.frame.TableOne(tab2), size=11, first.strip=T, hide.no="no",
format="html",caption= "Table 2. Summary descriptives, by accused",
smd=T, test=T, varLabels=T,noSpaces=T, printToggle=T, dropEqual=F) %>% #,col.names=c("Variables","Residential", "Ambulatory", "p-value")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover","condensed"),font_size= 10) %>%
row_spec(1, bold=T, italic=T,color="black",hline_after=T,extra_latex_after="\\arrayrulecolor{white}",font_size= 11) %>%
kableExtra::add_footnote(c("Note. Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;", "Categorical variables are presented as number (%)"), notation = "none")%>%
kableExtra::scroll_box(width = "100%", height = "375px")| characteristic | level | NO | SI | p | test | SMD |
|---|---|---|---|---|---|---|
| n | 191165 | 377132 | ||||
| encontrado_como_victima (%) | NO | 0 ( 0.0) | 376766 ( 99.9) | <0.001 | 45.374 | |
| SI | 191165 (100.0) | 366 ( 0.1) | ||||
| encontrado_como_imputado (%) | NO | 191165 (100.0) | 0 ( 0.0) | <0.001 | NaN | |
| SI | 0 ( 0.0) | 377132 (100.0) | ||||
| tipo_sujeto_vic (%) | 1 | 0 ( 0.0) | 306 ( 0.1) | <0.001 | 0.325 | |
| 15 | 0 ( 0.0) | 226 ( 0.1) | ||||
| 19 | 0 ( 0.0) | 330 ( 0.1) | ||||
| 2 | 0 ( 0.0) | 2 ( 0.0) | ||||
| 3 | 0 ( 0.0) | 13 ( 0.0) | ||||
| 5 | 0 ( 0.0) | 4 ( 0.0) | ||||
| 6 | 191165 (100.0) | 358197 ( 95.0) | ||||
| 8 | 0 ( 0.0) | 1 ( 0.0) | ||||
| 0 ( 0.0) | 18053 ( 4.8) | |||||
| gls_tipo_sujeto_vic (%) | ABOGADO QUERELLANTE | 0 ( 0.0) | 1 ( 0.0) | <0.001 | 0.325 | |
| DENUNCIADO | 0 ( 0.0) | 2 ( 0.0) | ||||
| DENUNCIANTE | 0 ( 0.0) | 306 ( 0.1) | ||||
| IMPUTADO | 0 ( 0.0) | 13 ( 0.0) | ||||
| QUERELLANTE | 0 ( 0.0) | 4 ( 0.0) | ||||
| TESTIGO | 0 ( 0.0) | 226 ( 0.1) | ||||
| VICTIMA | 191165 (100.0) | 358197 ( 95.0) | ||||
| VICTIMA 108i.2 | 0 ( 0.0) | 330 ( 0.1) | ||||
| 0 ( 0.0) | 18053 ( 4.8) | |||||
| reg (mean (SD)) | 11.01 (5.38) | 10.54 (5.52) | <0.001 | 0.086 | ||
| gls_region (%) | RM Centro Norte | 27199 ( 14.2) | 51422 ( 13.6) | <0.001 | 0.083 | |
| RM Occidente | 23122 ( 12.1) | 40733 ( 10.8) | ||||
| RM Oriente | 14124 ( 7.4) | 29995 ( 8.0) | ||||
| RM Sur | 23836 ( 12.5) | 40085 ( 10.6) | ||||
| 102884 ( 53.8) | 214897 ( 57.0) | |||||
| tipo_termino (%) | OTROS T¿RMINOS | 12081 ( 6.3) | 19985 ( 5.3) | <0.001 | 0.570 | |
| SALIDA JUDICIAL | 68548 ( 35.9) | 237148 ( 62.9) | ||||
| SALIDA NO JUDICIAL | 110534 ( 57.8) | 119980 ( 31.8) | ||||
| 2 ( 0.0) | 19 ( 0.0) | |||||
| agrupa_terminos (%) | ACUERDO REPARATORIO | 2926 ( 1.5) | 10753 ( 2.9) | <0.001 | 0.966 | |
| AGRUPACI¿N A OTRO CASO | 11361 ( 5.9) | 16919 ( 4.5) | ||||
| ANULACI¿N ADMINISTRATIVA | 368 ( 0.2) | 1240 ( 0.3) | ||||
| ARCHIVO PROVISIONAL | 87494 ( 45.8) | 62180 ( 16.5) | ||||
| DECISI¿N DE NO PERSEVERAR | 9942 ( 5.2) | 23263 ( 6.2) | ||||
| FACULTAD PARA NO INVESTIGAR | 26158 ( 13.7) | 16093 ( 4.3) | ||||
| INCOMPETENCIA | 968 ( 0.5) | 1465 ( 0.4) | ||||
| OTRAS CAUSALES DE SUSPENSI¿N | 77 ( 0.0) | 841 ( 0.2) | ||||
| OTRAS CAUSALES DE T¿RMINO | 275 ( 0.1) | 985 ( 0.3) | ||||
| PRINCIPIO DE OPORTUNIDAD | 12130 ( 6.3) | 33072 ( 8.8) | ||||
| SENTENCIA DEFINITIVA ABSOLUTORIA | 2898 ( 1.5) | 8954 ( 2.4) | ||||
| SENTENCIA DEFINITIVA CONDENATORIA | 18790 ( 9.8) | 138399 ( 36.7) | ||||
| SOBRESEIMIENTO DEFINITIVO | 6697 ( 3.5) | 21591 ( 5.7) | ||||
| SOBRESEIMIENTO DEFINITIVO 240 | 9547 ( 5.0) | 35081 ( 9.3) | ||||
| SOBRESEIMIENTO TEMPORAL | 143 ( 0.1) | 514 ( 0.1) | ||||
| SUSPENSI¿N CONDICIONAL DEL PROCEDIMIENTO | 1389 ( 0.7) | 5763 ( 1.5) | ||||
| 2 ( 0.0) | 19 ( 0.0) | |||||
| gls_materia (%) | ABANDONO DE ARMAS O ELEMENTOS SUJETAS A CONTROL. ART. 14 A. | 4 ( 0.0) | 4 ( 0.0) | <0.001 | 1.286 | |
| ABANDONO DE CÓNYUGE O DE PARIENTES ENFERMOS. ART. 352. | 6 ( 0.0) | 7 ( 0.0) | ||||
| ABANDONO DE NIÑOS | 1 ( 0.0) | 40 ( 0.0) | ||||
| ABANDONO O MALTRATO ANIMAL ART.291 BIS. | 92 ( 0.0) | 113 ( 0.0) | ||||
| ABIGEATO | 97 ( 0.1) | 154 ( 0.0) | ||||
| ABORTO COMETIDO POR FACULTATIVO POR CAUSALES NO REGULADAS | 1 ( 0.0) | 0 ( 0.0) | ||||
| ABORTO CONSENTIDO POR CAUSLAES N/REGULADAS ART 342 N 3 Y 344 | 7 ( 0.0) | 14 ( 0.0) | ||||
| ABORTO SIN CONSENTIMIENTO | 15 ( 0.0) | 2 ( 0.0) | ||||
| ABUSO DE FIRMA EN BLANCO | 6 ( 0.0) | 2 ( 0.0) | ||||
| ABUSO SEX C/CONTACTO CORP. A MENOR DE 14 AÑOS ART 366 BIS | 121 ( 0.1) | 940 ( 0.2) | ||||
| ABUSO SEX MAYOR 14/MENOR 18 CON CIRCUNS ESTUPRO ART 366 INC2 | 76 ( 0.0) | 105 ( 0.0) | ||||
| ABUSO SEX MAYOR DE 14 AÑOS POR SORPRESA Y/O S/CONSENTIM. ART | 1 ( 0.0) | 0 ( 0.0) | ||||
| ABUSO SEX. S/CONTAC MAYOR 14 MENOR 18 ART 366 QUAT INC 3,4,5 | 116 ( 0.1) | 198 ( 0.1) | ||||
| ABUSO SEX. S/CONTACTO CORP. MENOR 14 ART 366 QUAT. INC 1 Y 2 | 75 ( 0.0) | 528 ( 0.1) | ||||
| ABUSO SEXUAL ADULTO | 2 ( 0.0) | 2 ( 0.0) | ||||
| ABUSO SEXUAL CALIFICADO (CON OBJETOS O ANIMALES)ART.365 BIS | 23 ( 0.0) | 13 ( 0.0) | ||||
| ABUSO SEXUAL DE MAYOR DE 14 (CON CIRC. DE VIOLACIÓN) ART 366 | 622 ( 0.3) | 333 ( 0.1) | ||||
| ABUSOS CONTRA PARTICULARES | 1 ( 0.0) | 1 ( 0.0) | ||||
| ABUSOS CONTRA PARTICULARES.ARTS. 255. | 311 ( 0.2) | 9 ( 0.0) | ||||
| ACCESO, DIVULG.Y USO INDEB.INF.GENÉTICA CON OCASIÓN TRAT.ADN | 1 ( 0.0) | 0 ( 0.0) | ||||
| ACCIDENTE CON RESUL. MUERTE O LES.GRAVES ART.196 C LEY 18290 | 283 ( 0.1) | 192 ( 0.1) | ||||
| ADQ. O ALMACENAMIENTO MAT.PORNOGRÁFICO INF.ART.374 BIS INC 2 | 7 ( 0.0) | 12 ( 0.0) | ||||
| ALLANAMIENTOS IRREGULARES | 11 ( 0.0) | 0 ( 0.0) | ||||
| AMENAZA A FISCAL O DEFENSOR EN DESEMP. DE FUNC. ART 268 QUIN | 2 ( 0.0) | 8 ( 0.0) | ||||
| AMENAZA CON ARMA (FALTA) ART. 494 Nº4 CÓDIGO PENAL | 637 ( 0.3) | 1086 ( 0.3) | ||||
| AMENAZA GENDARME EN DESEMP. DE FUNC. ART 15 D DL 2589 | 28 ( 0.0) | 186 ( 0.0) | ||||
| AMENAZAR U OFENDER A FUNC DE INVESTIGACIONES. ART. 17 quáter | 12 ( 0.0) | 98 ( 0.0) | ||||
| AMENAZAS A CARABINEROS | 29 ( 0.0) | 1535 ( 0.4) | ||||
| AMENAZAS CONDIC. CONTRA PERSONAS Y PROP. ART. 296 1 y 2, 297 | 2879 ( 1.5) | 5463 ( 1.4) | ||||
| AMENAZAS DE ATENTADOS CONTRA PERSONAS Y PROPIEDADES | 298 ( 0.2) | 1010 ( 0.3) | ||||
| AMENAZAS SIMPLES CONTRA PERSONAS Y PROPIEDADES ART. 296 Nº3. | 28137 ( 14.7) | 53380 ( 14.2) | ||||
| APERTURA, REG. O INTERCEPTACIÓN DE CORRESP. ART.146 Y 156 | 4 ( 0.0) | 11 ( 0.0) | ||||
| APREMIOS ILEG. COMETIDOS POR EMPL. PÚBLICOS. (ART. 150 D) | 282 ( 0.1) | 1 ( 0.0) | ||||
| APREMIOS ILEGÍTIMOS CON CUASIDELITO (ART. 150 E N° 3) | 5 ( 0.0) | 1 ( 0.0) | ||||
| APROPIACIÓN Y DISTRACCIÓN DE COTIZACIONES PREVISIONALES | 804 ( 0.4) | 114 ( 0.0) | ||||
| APROPIACIÓN DE CABLES DE TENDIDO ELÉC.O DE COM.ART.443 INC 2 | 28 ( 0.0) | 14 ( 0.0) | ||||
| APROPIACIÓN INDEBIDA (INCL. DEPOSITARIO ALZADO) ART.470 Nº1 | 248 ( 0.1) | 485 ( 0.1) | ||||
| APROPIACION INDEBIDA ART.470 N°1 | 1561 ( 0.8) | 2741 ( 0.7) | ||||
| ARROJAMIENTO DE PIEDRAS U OTROS OBJETOS (496 Nº26 Cód.Penal) | 23 ( 0.0) | 230 ( 0.1) | ||||
| ASOCIACION ILICITA ART. 28 LEY 19.913 | 2 ( 0.0) | 2 ( 0.0) | ||||
| ASOCIACIONES ILÍCITAS LEY DE DROGAS ART. 16 LEY Nº 20.000. | 0 ( 0.0) | 13 ( 0.0) | ||||
| ASOCIACIONES ILÍCITAS. ARTS. 292 AL 295 BIS. | 1 ( 0.0) | 15 ( 0.0) | ||||
| ATENTADO A VEH.MOT. EN CIRC. CON OBJ.CONTUNDENTE U OTRO SEM. | 26 ( 0.0) | 81 ( 0.0) | ||||
| ATENTADO CONTRA JEFE DE ESTADO O AUT. PÚB. ART.2 Nº3 L.18314 | 0 ( 0.0) | 5 ( 0.0) | ||||
| ATENTADO EXPLOSIVO O INCENDIARIO | 2 ( 0.0) | 2 ( 0.0) | ||||
| ATENTADOS Y AMENAZAS CONTRA LA AUTORIDAD. ART. 261 Nº1 Y 264 | 26 ( 0.0) | 226 ( 0.1) | ||||
| AUXILIO AL SUICIDIO | 13 ( 0.0) | 0 ( 0.0) | ||||
| BIGAMIA | 0 ( 0.0) | 1 ( 0.0) | ||||
| CALUMNIA (ACCIÓN PRIVADA). ART. 412 AL 415. | 8 ( 0.0) | 6 ( 0.0) | ||||
| CASTRACIÓN Y MUTILACIÓN | 0 ( 0.0) | 1 ( 0.0) | ||||
| CAUSAR LA MUERTE FUNC PDI ART. 17 LOC PDI | 0 ( 0.0) | 2 ( 0.0) | ||||
| CAZA Y COMERCIALIZACIÓN DE ESPECIES PROH. (Art.31 Ley 19473) | 0 ( 0.0) | 7 ( 0.0) | ||||
| CELEBRACIÓN DE CONTRATO SIMULADO. ART. 471 Nº 2. | 4 ( 0.0) | 1 ( 0.0) | ||||
| COHECHO COMETIDO POR EMPLEADO PÚBLICO ART 248, 248 BIS Y 249 | 6 ( 0.0) | 53 ( 0.0) | ||||
| COHECHO O SOBORNO COMETIDO POR PARTICULAR. ART. 250 | 0 ( 0.0) | 24 ( 0.0) | ||||
| COMERCIALIZACIÓN MAT. PORNOGRÁFICO ELAB.UTIL. MEN.DE 18 AÑOS | 1 ( 0.0) | 0 ( 0.0) | ||||
| COMERCIO CLANDESTINO | 0 ( 0.0) | 10 ( 0.0) | ||||
| CONDUC BAJO INFLUENCIA DEL ALCOHOL ART.196 C LEY 18290 TRÁNS | 1 ( 0.0) | 2 ( 0.0) | ||||
| CONDUC EBRIDAD C/LESIONS MENOS GRAVESART196 INC2 LEY TRAN | 21 ( 0.0) | 90 ( 0.0) | ||||
| CONDUC EBRIEDAD C/LESIONS GRAVES ART 196 INC2LEY TRANS | 45 ( 0.0) | 98 ( 0.0) | ||||
| CONDUC EBRIEDAD C/LESIONS GRAVÍSIMAS ART196 INC3LEY TRANS | 5 ( 0.0) | 17 ( 0.0) | ||||
| CONDUC EBRIEDAD C/RESULT MUERTE ART196INC3 LEY TRANSITO | 5 ( 0.0) | 24 ( 0.0) | ||||
| CONDUC EBRIEDAD C/SUSP LICENCIA ART 196,209 LEY TRANSITO | 5 ( 0.0) | 210 ( 0.1) | ||||
| CONDUC. BAJO LA INFLUENCIA DEL ALCOHOL CAUSANDO LES.MEN.GRAV | 20 ( 0.0) | 66 ( 0.0) | ||||
| CONDUC. INFLUENCIA ALCOHOL CAUSANDO LES.GRAVÍSIMAS O MUERTE | 7 ( 0.0) | 9 ( 0.0) | ||||
| CONDUCC. INF. ALCOHOL CON O SIN DAÑO O LES. LEVE 193 INC 1 | 81 ( 0.0) | 964 ( 0.3) | ||||
| CONDUCCIÓN BAJO LA INFLUENCIA DEL ALCOHOL CAUSANDO LES.GRAV | 27 ( 0.0) | 84 ( 0.0) | ||||
| CONDUCCIÓN EBRIEDAD CON O SIN DAÑO O LES. LEVE 196 INC. 1 | 533 ( 0.3) | 8524 ( 2.3) | ||||
| CONDUCCIÓN ESTADO DE EBRIEDAD CON RESULTADO DE DAÑOS. | 94 ( 0.0) | 543 ( 0.1) | ||||
| CONDUCCIÓN ESTADO DE EBRIEDAD CON RESULTADO DE LESIONES. | 283 ( 0.1) | 638 ( 0.2) | ||||
| CONDUCCIÓN ESTADO DE EBRIEDAD CON RESULTADO DE MUERTE | 6 ( 0.0) | 73 ( 0.0) | ||||
| CONDUCCIÓN SIN LA LICENCIA DEBIDA ART 196 D LEY 18.290 | 15 ( 0.0) | 1436 ( 0.4) | ||||
| CONDUCIR INFL ALCOHOL CAUSANDO LES GRAVÍSIMAS ART 193 INC 4 | 0 ( 0.0) | 1 ( 0.0) | ||||
| CONDUCIR INFL ALCOHOL CAUSANDO MUERTE. ART 193 INC 4 | 0 ( 0.0) | 3 ( 0.0) | ||||
| CONDUCIR VEHICULO CON SANCIÓN VIGENTE. ART.209 LEY 18.290. | 0 ( 0.0) | 310 ( 0.1) | ||||
| CONNIVENCIA FUGA Y EVASIÓN CULPABLE DETENIDO ART. 299 A 304 | 0 ( 0.0) | 5 ( 0.0) | ||||
| CONSUMO DE BEBIDAS ALCOHÓLICAS EN LA VÍA PÚBLICA | 0 ( 0.0) | 2 ( 0.0) | ||||
| CONSUMO DE DROGAS (ART. 41) | 0 ( 0.0) | 36 ( 0.0) | ||||
| CONSUMO/PORTE DE DROGAS EN LUGARES CALIFICADOS (ART.51) | 5 ( 0.0) | 5186 ( 1.4) | ||||
| CONSUMO/PORTE EN LUG. PÚB.O PRIV. CON PREV.CONCIERTO(ART.50) | 12 ( 0.0) | 22332 ( 5.9) | ||||
| CONTRA SALUD PÚBLICA. ARTS. 313 D AL 315 y ART. 317. | 1 ( 0.0) | 6 ( 0.0) | ||||
| CONTRA SALUD PUBLICA. ARTS. 313 A Y 313 B | 4 ( 0.0) | 4 ( 0.0) | ||||
| CONTRA SALUD PÚBLICA. ARTS. 313 AL 318. | 2 ( 0.0) | 7 ( 0.0) | ||||
| CONTRABANDO DE ESPECIES EXÓTICAS (ART. 11, LEY 20.962) | 0 ( 0.0) | 1 ( 0.0) | ||||
| CONTRABANDO. INFRACCIÓN A ORDZA ADUANAS ART 168 LEY 20780 | 0 ( 0.0) | 133 ( 0.0) | ||||
| CORTE DESTR. ARBOLES RGMTO ART. 21 DCTO.4363 1931 Y L. BOSQ. | 1 ( 0.0) | 3 ( 0.0) | ||||
| CUASIDELITO DE HOMICIDIO COMETIDO POR PROFES. DE LA SALUD | 16 ( 0.0) | 2 ( 0.0) | ||||
| CUASIDELITO DE HOMICIDIO. CÓDIGO AGRUPADOR. ART. 492 INC. 2º | 118 ( 0.1) | 115 ( 0.0) | ||||
| CUASIDELITO DE LESIONES COMETIDOS POR PROFES. DE LA SALUD | 27 ( 0.0) | 2 ( 0.0) | ||||
| CUASIDELITO DE LESIONES. ART 490, 491 INC 2° Y 492. | 1832 ( 1.0) | 1683 ( 0.4) | ||||
| CUASIDELITO VEHICULO MOTORIZADO LEY TRANSITO | 39 ( 0.0) | 35 ( 0.0) | ||||
| CULTIVO/COSECHA ESPEC. VEGETALES PRODUCTORAS ESTUPEF (ART.8) | 9 ( 0.0) | 621 ( 0.2) | ||||
| DAÑO FALTA (495 N° 21 Código Penal) | 471 ( 0.2) | 1424 ( 0.4) | ||||
| DAÑOS A MONUMENTOS NACIONALES ART.38 LEY 17.288 | 1 ( 0.0) | 0 ( 0.0) | ||||
| DAÑOS CALIFICADOS. ART. 485 Y 486 | 128 ( 0.1) | 306 ( 0.1) | ||||
| DAÑOS O APROPIACIÓN SOBRE MONUM. NAC. ART. 38-38 BIS L.17288 | 2 ( 0.0) | 15 ( 0.0) | ||||
| DAÑOS SIMPLES. ART. 487. | 4701 ( 2.5) | 10195 ( 2.7) | ||||
| DECLARACION MALICIOSA IMPSTOS 97N4(EXCEPTO INC3)COD TRIB | 0 ( 0.0) | 1 ( 0.0) | ||||
| DEJAR ANIMALES SUELTOS (496 Nº 17 Código Penal) | 187 ( 0.1) | 124 ( 0.0) | ||||
| DELITO DESÓRDENES PÚBLICOS ART. 269 (NO FALTA DEL CÓD 13035) | 36 ( 0.0) | 2452 ( 0.7) | ||||
| DELITOS C/ LA LIBERTAD AMBULATORIA Y DERECHO DE ASOCIACIÓN | 0 ( 0.0) | 1 ( 0.0) | ||||
| DELITOS C/ LA VIDA Y PRIVACIDAD DE CONVERSACIONES 161 A Y B | 17 ( 0.0) | 13 ( 0.0) | ||||
| DELITOS CONTEMPLADOS EN OTROS TEXTOS LEGALES | 36 ( 0.0) | 48 ( 0.0) | ||||
| DELITOS CONTENIDOS EN LEYES DE PRENDA ESP. L.20190 Y 18690 | 0 ( 0.0) | 3 ( 0.0) | ||||
| DELITOS CONTRA LA LEY DE BOSQUE NATIVO. LEY 20.283 | 1 ( 0.0) | 2 ( 0.0) | ||||
| DELITOS CONTRA LEY DE PROPIEDAD INDUSTRIAL | 0 ( 0.0) | 1 ( 0.0) | ||||
| DELITOS CONTRA LEY DE PROPIEDAD INTELECTUAL | 0 ( 0.0) | 41 ( 0.0) | ||||
| DELITOS DE LA LEY DE SOCIEDADES ANÓNIMAS ART. 134 LEY 18.046 | 0 ( 0.0) | 1 ( 0.0) | ||||
| DELITOS MARCARIOS | 0 ( 0.0) | 3 ( 0.0) | ||||
| DELITOS QUE CONTEMPLA EL CÓDIGO TRIBUTARIO. ARTS. 97 AL 114 | 0 ( 0.0) | 20 ( 0.0) | ||||
| DEMÁS DELITOS CONTRA LEY DE PROPIEDAD INDUSTRIAL | 0 ( 0.0) | 306 ( 0.1) | ||||
| DEPOSITARIO ALZADO ART. 444 CPC | 5 ( 0.0) | 10 ( 0.0) | ||||
| DESACATO (Art. 240 Código de Procedimiento Civil) | 1975 ( 1.0) | 10150 ( 2.7) | ||||
| DESATENDER EL LLAMADO A RECLUTAMIENTO DL 2306. Art. 72 | 0 ( 0.0) | 117 ( 0.0) | ||||
| DESÓRDENES EN ESPECTÁCULOS PÚBLICOS 494 Nº1 CÓDIGO PENAL | 2 ( 0.0) | 351 ( 0.1) | ||||
| DESTRUCCIÓN O ALTERACIÓN DE DESLINDES. ART. 462. | 2 ( 0.0) | 2 ( 0.0) | ||||
| DETENCION, DESTIERRO O ARRESTO IRREGULAR ART. 148 | 38 ( 0.0) | 13 ( 0.0) | ||||
| DEUDOR/GTE/OTROS PERJUDICAN ACREEDORES Art463, 463bis/ter | 0 ( 0.0) | 2 ( 0.0) | ||||
| DISENSIONES DOMÉSTICAS 495 Nº6 CÓDIGO PENAL | 30 ( 0.0) | 41 ( 0.0) | ||||
| DISPAROS INJUSTIFICADOS EN VÍA PÚBLICA (ART 14 D INC. FINAL) | 6 ( 0.0) | 11 ( 0.0) | ||||
| EJERCICIO ILEGAL DE LA PROFESIÓN. ART. 213 INC 1 | 10 ( 0.0) | 29 ( 0.0) | ||||
| ELABORACIÓN ILEGAL DE DROGAS O SUST. SICOT. ART.1 L.20000 | 0 ( 0.0) | 7 ( 0.0) | ||||
| ENSEÑANZA NO AUTORIZ. DE ARTES MARCIALES (Art. 5 Ley 18.356) | 0 ( 0.0) | 2 ( 0.0) | ||||
| ESPIONAJE INFORMÁTICO ART. 2 Y 4 LEY 19223 | 13 ( 0.0) | 29 ( 0.0) | ||||
| ESTAFAS Y OTRAS DEFRAUDACIONES CONTRA PARTICULARES | 1606 ( 0.8) | 5262 ( 1.4) | ||||
| ESTUPRO | 77 ( 0.0) | 124 ( 0.0) | ||||
| EXACCIONES ILEGALES COMETIDAS POR FUNC. PÚB. ART.157 y 241. | 1 ( 0.0) | 2 ( 0.0) | ||||
| EXACCIONES ILEGALES COMETIDAS POR PARTICULARES | 0 ( 0.0) | 9 ( 0.0) | ||||
| EXPENDIO DE BEBIDAS ALCOHÓLICAS A MENORES. ART.42 LEY 19925 | 16 ( 0.0) | 22 ( 0.0) | ||||
| EXTORSION. ART. 438. | 6 ( 0.0) | 7 ( 0.0) | ||||
| EXTRANJ INGRESAN/TRATAN EGRESAR CLANDESTINAMENTE 69 DL1094 | 0 ( 0.0) | 1 ( 0.0) | ||||
| FABRICACIÓN, ACOPIO O COMERCIALIZACIÓN DE HILO CURADO. | 0 ( 0.0) | 1 ( 0.0) | ||||
| FALSA ALARMA INCENDIO, EMERGENCIA O CALAMIDAD ART 268 BIS | 0 ( 0.0) | 15 ( 0.0) | ||||
| FALSIF LICENCIAS MEDICAS O PENSIONES. ART.202 INC.2° Y 3° | 1 ( 0.0) | 4 ( 0.0) | ||||
| FALSIF MONEDA (PARA BILLETES CÓD 12031).ART 162 AL 214 CP | 9 ( 0.0) | 45 ( 0.0) | ||||
| FALSIF OBRAS PROTEGIDAS LEY PROP INTELECT ART.79 BIS | 1 ( 0.0) | 44 ( 0.0) | ||||
| FALSIF.PLACAS U OTROS DE INVESTIGACIONES. ART. 16 inciso 3 | 0 ( 0.0) | 8 ( 0.0) | ||||
| FALSIF/USO PASAPORTE O PERMISOS PORTE ARMAS ART 199 A 201 | 1 ( 0.0) | 0 ( 0.0) | ||||
| FALSIFICACIÓN DE BILLETES ART. 64 LEY ORGÁNICA B.CENTRAL | 26 ( 0.0) | 105 ( 0.0) | ||||
| FALSIFICACIÓN LIC.DE COND. Y OTRAS FALSIF. ART.196 B L.18290 | 3 ( 0.0) | 208 ( 0.1) | ||||
| FALSIFICACIÓN O USO MALICIOSO DE DOC. PRIVADOS ART.197 Y 198 | 143 ( 0.1) | 1648 ( 0.4) | ||||
| FALSIFICACION O USO MALICIOSO DE DOCUMENTOS PUBLICOS | 53 ( 0.0) | 483 ( 0.1) | ||||
| FALSO TEST, PERJURIO O DEN.CALUM. ART.206,209,210,211 Y 212 | 19 ( 0.0) | 49 ( 0.0) | ||||
| FALTA DE RESPETO A LA AUTORIDAD PÚBLICA 495 Nº4 CÓDIGO PENAL | 9 ( 0.0) | 901 ( 0.2) | ||||
| FEMICIDIO INTIMO ART. 390 BIS | 28 ( 0.0) | 20 ( 0.0) | ||||
| FINGIMIENTO DE CARGOS O PROFESIONES. ART. 213 INC. 2 | 4 ( 0.0) | 4 ( 0.0) | ||||
| FRAUDE ADUANERO. INFRACCIÓN ORDZA ADUANAS ART 169 LEY 20780 | 0 ( 0.0) | 5 ( 0.0) | ||||
| FRAUDE DE SUBVENCIONES ART 470 N°8 | 3 ( 0.0) | 8 ( 0.0) | ||||
| FRAUDES AL FISCO Y ORGANISMOS DEL ESTADO. ART.239 | 0 ( 0.0) | 5 ( 0.0) | ||||
| GANADO QUE ENTRA A PREDIO AJENO CAUSANDO DAÑOS | 5 ( 0.0) | 0 ( 0.0) | ||||
| GIRO DOLOSO CHEQUES (CUENTA CERRADA) ART. 22. DFL 707 | 1 ( 0.0) | 0 ( 0.0) | ||||
| GIRO DOLOSO CHEQUES (FALTA FONDOS). ART. 22. DFL 707 | 2 ( 0.0) | 0 ( 0.0) | ||||
| GIRO DOLOSO DE CHEQUES | 10 ( 0.0) | 84 ( 0.0) | ||||
| GIRO DOLOSO DE CHEQUES AC. PENAL PÜBLICA. ART. 42. DFL 707 | 0 ( 0.0) | 2 ( 0.0) | ||||
| HALLAZGO DE DROGAS | 7 ( 0.0) | 119 ( 0.0) | ||||
| HALLAZGO DE VEHÍCULO | 1251 ( 0.7) | 123 ( 0.0) | ||||
| HOMICIDIO | 431 ( 0.2) | 467 ( 0.1) | ||||
| HOMICIDIO CALIFICADO | 30 ( 0.0) | 27 ( 0.0) | ||||
| HOMICIDIO EN RIÑA O PELEA | 6 ( 0.0) | 23 ( 0.0) | ||||
| HOMICIDIO GENDARME ENDESEMP. DE FUNC. ART 15 A - DL 2859 | 0 ( 0.0) | 1 ( 0.0) | ||||
| HURTO AGRAVADO (ART. 447 CÓDIGO PENAL) | 90 ( 0.0) | 766 ( 0.2) | ||||
| HURTO DE BIENES PERTENECIENTES A REDES DE SUMINISTRO PÚBLICO | 25 ( 0.0) | 111 ( 0.0) | ||||
| HURTO DE HALLAZGO | 103 ( 0.1) | 153 ( 0.0) | ||||
| HURTO FALTA 494 BIS CÓDIGO PENAL | 1506 ( 0.8) | 34986 ( 9.3) | ||||
| HURTO SIMPLE | 8 ( 0.0) | 201 ( 0.1) | ||||
| HURTO SIMPLE POR UN VALOR DE 4 A 40 UTM | 3960 ( 2.1) | 8928 ( 2.4) | ||||
| HURTO SIMPLE POR UN VALOR DE MEDIA A 4 UTM | 5270 ( 2.8) | 31184 ( 8.3) | ||||
| HURTO SIMPLE POR UN VALOR SOBRE 40 UTM | 500 ( 0.3) | 789 ( 0.2) | ||||
| IMPEDIR EJER. DE FUNC. A INSP. MUNIC. ART 496 N° 3 C. PENAL | 1 ( 0.0) | 1 ( 0.0) | ||||
| INCENDIO | 0 ( 0.0) | 1 ( 0.0) | ||||
| INCENDIO CON PELIGRO PARA LAS PERSONAS (475 Y 476 N° 1 y 2) | 130 ( 0.1) | 157 ( 0.0) | ||||
| INCENDIO CON RESULTADO DE MUERTE Y/O LESIONES | 118 ( 0.1) | 37 ( 0.0) | ||||
| INCENDIO DE BOSQUES Art. 476 Nº 3 Y 4. | 59 ( 0.0) | 25 ( 0.0) | ||||
| INCENDIO SOLO CON DAÑOS O SIN PELIGRO DE PROP. ART.477 Y 478 | 581 ( 0.3) | 212 ( 0.1) | ||||
| INCESTO | 3 ( 0.0) | 6 ( 0.0) | ||||
| INDUCIR A UN MENOR A ABANDONAR EL HOGAR | 15 ( 0.0) | 38 ( 0.0) | ||||
| INFANTICIDIO | 0 ( 0.0) | 2 ( 0.0) | ||||
| INFRAC EN PRESTACION SALUD ART 168,174 INC FINAL DS 1 SALUD | 0 ( 0.0) | 3 ( 0.0) | ||||
| INFRAC LEY ORG.CONSTITUCIONAL SOBRE VOTAC POPULARES Y ESCRUT | 1 ( 0.0) | 6 ( 0.0) | ||||
| INFRAC.A LA SEG.SOC. DL3500 Y 869 LS.18020,18469,18933,19728 | 0 ( 0.0) | 3 ( 0.0) | ||||
| INFRACC. LEY GRAL DE TELECOM ART 36 B LETRAS A, B, C Y D | 3 ( 0.0) | 16 ( 0.0) | ||||
| INFRACCIÓN A LA LEY ELECTORAL. ART. 128 AL 141. | 1 ( 0.0) | 5 ( 0.0) | ||||
| INFRACCIÓN ARTÍCULO 454 | 1 ( 0.0) | 468 ( 0.1) | ||||
| INFRACCIÓN LEY 11.564 DE MATADEROS CLANDESTINOS. ART. 1. | 0 ( 0.0) | 21 ( 0.0) | ||||
| INFRACCIÓN LEY 18.892 DE PESCA. ART.135 AL 139. | 0 ( 0.0) | 14 ( 0.0) | ||||
| INFRACCIÓN NORMAS INHUMACIONES Y EXHUMAC. ART.320,321 Y 322 | 0 ( 0.0) | 3 ( 0.0) | ||||
| INFRACCIÓN ORDENANZA ADUANAS (FRAUDE Y CONTRABANDO) ART.176. | 0 ( 0.0) | 157 ( 0.0) | ||||
| INFRACCION LEY DE QUIEBRAS. ART.218 AL 221. | 1 ( 0.0) | 0 ( 0.0) | ||||
| INFRACCIONES A LA LEY DE SEG.NUCLEAR (Art.41 a 47 Ley 18302) | 3 ( 0.0) | 3 ( 0.0) | ||||
| INFRACCIONES AL CÓD. AERONÁUTICO (Arts. 190, 194 bis, 195 a | 0 ( 0.0) | 4 ( 0.0) | ||||
| INJURIA (ACCIÓN PRIVADA). ART. 416 AL 420. | 14 ( 0.0) | 6 ( 0.0) | ||||
| INJURIAS Y CALUMNIAS POR MEDIOS DE COMUNICACIÓN SOCIAL | 9 ( 0.0) | 6 ( 0.0) | ||||
| INSOLVENCIA PUNIBLE (ALZAMIENTO DE BIENES) | 0 ( 0.0) | 1 ( 0.0) | ||||
| INSTAL.INDEB.SEÑALES DE TRÁN.O BARRERAS ART.196 A1 LEY 18290 | 0 ( 0.0) | 1 ( 0.0) | ||||
| INTERRUPCIÓN SERV. ELECT.(Art.134 DFL.1,1982,Ley Serv.Eléc.) | 0 ( 0.0) | 2 ( 0.0) | ||||
| INUTILIZAR DISPOSIT MONITOREO TELEMAT ART 23 SEXIES L18216 | 0 ( 0.0) | 1 ( 0.0) | ||||
| LAVADO DE DINERO PERSONA NATURAL ART. 27 LEY 19.913 | 1 ( 0.0) | 4 ( 0.0) | ||||
| LESIONES GRAVES | 4774 ( 2.5) | 2295 ( 0.6) | ||||
| LESIONES GRAVES GRAVÍSIMAS. ART. 397 Nº1 | 81 ( 0.0) | 48 ( 0.0) | ||||
| LESIONES LEVES | 28336 ( 14.8) | 25450 ( 6.7) | ||||
| LESIONES LEVES 494 Nº 5 CODIGO PENAL | 299 ( 0.2) | 298 ( 0.1) | ||||
| LESIONES MENOS GRAVES | 23928 ( 12.5) | 30866 ( 8.2) | ||||
| LESIONES O DAÑOS EN FÚTBOL PROFESIONAL ART 12 INC 1 19327 | 2 ( 0.0) | 7 ( 0.0) | ||||
| LEY RESPONSABILIDAD PENAL PERSONAS JURIDICAS LEY 20393 | 2 ( 0.0) | 0 ( 0.0) | ||||
| LOTEOS IRREGULARES (Art.138 DFL 458,1975,Ley Gral.Urb.y Con) | 2 ( 0.0) | 0 ( 0.0) | ||||
| LOTERIA ILEGAL, CASA JUEGO Y PRÉSTAMO PRENDA ARTS 275 AL 283 | 3 ( 0.0) | 10 ( 0.0) | ||||
| MALTRATO COMETIDO POR GARANTE ART. 403 BIS INC FINAL | 0 ( 0.0) | 16 ( 0.0) | ||||
| MALTRATO CORPORAL A PERSONAS VULNERABLES ART 403 BIS INC 1° | 4 ( 0.0) | 135 ( 0.0) | ||||
| MALTRATO DE OBRA A CARAB ART. 416 BIS COD DE JUST MILITAR | 16 ( 0.0) | 1655 ( 0.4) | ||||
| MALTRATO DE OBRA FUNC PDI CON/SIN LESIONES 17 bis LOC PDI | 1 ( 0.0) | 131 ( 0.0) | ||||
| MALTRATO HABITUAL (VIOLENCIA INTRAFAMILIAR) | 1637 ( 0.9) | 7802 ( 2.1) | ||||
| MALTRATO OBRA FISCAL, DEFENSOR EN DESEMP. DE FUNC. ART. 268 | 1 ( 0.0) | 9 ( 0.0) | ||||
| MALTRATO OBRA GENDARME EN DESEMP. DE FUNC.ART 15 B,C DL 2859 | 9 ( 0.0) | 132 ( 0.0) | ||||
| MALVERSACION DE CAUDALES PUBLICOS. ARTS 233, 234, 235 y 236 | 0 ( 0.0) | 31 ( 0.0) | ||||
| MARCHARSE SITIO SUCESO S/AUXILIAR VÍCTIMA ART 195 INC 2 y 3 | 10 ( 0.0) | 24 ( 0.0) | ||||
| MATAR A CARABINERO EN EJERCICIO DE SUS FUNCS. ART.416 C.J.M. | 0 ( 0.0) | 10 ( 0.0) | ||||
| MICROTRÁFICO (TRÁFICO DE PEQUEÑAS CANTID. ART. 4 LEY 20000) | 42 ( 0.0) | 5477 ( 1.5) | ||||
| MUERTES Y HALLAZGO DE CADÁVER | 1508 ( 0.8) | 17 ( 0.0) | ||||
| NEGATIVA A EFECTUARSE EXAMEN. ART. 195 BIS LEY DE TRANSITO | 0 ( 0.0) | 189 ( 0.1) | ||||
| NO DAR CUENTA DE ACCIDENTE DE TRÁNSITO ART. 96 D 1 LEY 18290 | 20 ( 0.0) | 92 ( 0.0) | ||||
| NO DEFINIDO | 0 ( 0.0) | 6 ( 0.0) | ||||
| NOMBRAMIENTOS ILEGALES | 0 ( 0.0) | 1 ( 0.0) | ||||
| OBSTR INVESTIGACION POR FISCAL O ASISTENTE MP. ART.269 TER | 0 ( 0.0) | 2 ( 0.0) | ||||
| OBSTRUCCIÓN A LA INVESTIGACIÓN. ART. 269 BIS. | 22 ( 0.0) | 56 ( 0.0) | ||||
| OBSTRUCCIÓN A LA JUSTICIA CON OCASIÓN DE TRATAMIENTO DE ADN | 1 ( 0.0) | 2 ( 0.0) | ||||
| OBTENCIÓN DE SERVICIOS SEXUALES DE MENORES. ART. 367 TER. | 14 ( 0.0) | 2 ( 0.0) | ||||
| OBTENCIÓN DECLARACIONES FORZADAS ART.19 DL 2460 LEY ORG. INV | 0 ( 0.0) | 1 ( 0.0) | ||||
| OBTENCION FRAUDULENTA DE CREDITOS. ART. 160. DFL 252 DE 1960 | 0 ( 0.0) | 16 ( 0.0) | ||||
| OCULTACIÓN DE IDENT. CONTROL PREV. 496 Nº 5 Y 12 LEY 20.931 | 6 ( 0.0) | 372 ( 0.1) | ||||
| OCULTACIÓN DE IDENTIDAD 496 Nº5 CÓDIGO PENAL | 23 ( 0.0) | 843 ( 0.2) | ||||
| OCULTACIÓN IDENT. EN CONTROL INVESTIGATIVO 496 N°5 Y 85 CPP | 0 ( 0.0) | 130 ( 0.0) | ||||
| OCULTACION O ENTR. DE INFO. FALSA A FNE. ART. 39 h) D.L. 211 | 0 ( 0.0) | 1 ( 0.0) | ||||
| OCULTAMIENTO DE PLACA PATENTE (ART. 192 LETRA E) | 1 ( 0.0) | 24 ( 0.0) | ||||
| OFENSAS AL PUDOR (495 Nº 5 Código Penal) | 87 ( 0.0) | 331 ( 0.1) | ||||
| OMISIÓN DE DENUNCIAR POR FUNCIONARIO PÚB. LEY 20.000 ART. 13 | 3 ( 0.0) | 0 ( 0.0) | ||||
| OPONERSE A ACCIÓN DE LA AUTORIDAD PÚBLICA O SUS AGENTES | 0 ( 0.0) | 435 ( 0.1) | ||||
| OTORG. IRREGULAR DE DOCTOS (Art. 196 a Ley 18.290 Tráns) | 1 ( 0.0) | 4 ( 0.0) | ||||
| OTRAS FALTAS CÓDIGO PENAL | 400 ( 0.2) | 2472 ( 0.7) | ||||
| OTRAS FALTAS LEYES ESPECIALES | 0 ( 0.0) | 4 ( 0.0) | ||||
| OTRAS FALTAS Y DELITOS DE LA LEY 19.733 | 1 ( 0.0) | 1 ( 0.0) | ||||
| OTRAS INFRACCIÓN LEY 18.892 DE PESCA | 0 ( 0.0) | 12 ( 0.0) | ||||
| OTRAS INFRACCIONES A LA LEY DEL BANCO CENTRAL | 4 ( 0.0) | 15 ( 0.0) | ||||
| OTRAS INFRACCIONES A LA ORDENANZA ADUANAS. LEY 20.780 | 0 ( 0.0) | 37 ( 0.0) | ||||
| OTRAS INFRACCIONES AL CÓDIGO JUSTICIA MILITAR | 10 ( 0.0) | 29 ( 0.0) | ||||
| OTRAS INFRACCIONES CONTRA LA LEY DE ALCOHOLES | 0 ( 0.0) | 1 ( 0.0) | ||||
| OTROS ABUSOS CONTRA PARTICULARES.ART. 256,257,258,259 | 9 ( 0.0) | 8 ( 0.0) | ||||
| OTROS DE LOS CUASIDELITOS | 33 ( 0.0) | 9 ( 0.0) | ||||
| OTROS DEL C/ ORDEN FAMILIA, MORALIDAD Pº, INTEGRIDAD SEXUAL | 25 ( 0.0) | 44 ( 0.0) | ||||
| OTROS DELITOS EMPLEADOS PÚBLICOS EN DESEMPEÑO DE SUS CARGOS | 12 ( 0.0) | 2 ( 0.0) | ||||
| OTROS DELITOS C/ FE PÚBLICA, FALSIFIC, FALSO TEST Y PERJURIO | 0 ( 0.0) | 31 ( 0.0) | ||||
| OTROS DELITOS C/ ORDEN Y SEGURIDAD Pº COMETIDOS PARTICULARES | 1 ( 0.0) | 24 ( 0.0) | ||||
| OTROS DELITOS CONTEMPLADOS EN LEY DE PROPIEDAD INTELECTUAL | 5 ( 0.0) | 936 ( 0.2) | ||||
| OTROS DELITOS CONTRA LA LEY DEL TRÁNSITO | 66 ( 0.0) | 562 ( 0.1) | ||||
| OTROS DELITOS CONTRA LA PROPIEDAD | 216 ( 0.1) | 351 ( 0.1) | ||||
| OTROS DELITOS CONTRA LAS PERSONAS | 319 ( 0.2) | 186 ( 0.0) | ||||
| OTROS DELITOS DE LA LEY 20.000 | 13 ( 0.0) | 1465 ( 0.4) | ||||
| OTROS DELITOS DE LA LEY DE CONTROL DE ARMAS (LEY 17.798) | 31 ( 0.0) | 121 ( 0.0) | ||||
| OTROS DELITOS LEY 19.327 SOBRE VIOLENCIA EN LOS ESTADIOS | 1 ( 0.0) | 5 ( 0.0) | ||||
| OTROS DELITOS LEY DE CUENTAS CORRIENTES BANCARIAS Y CHEQUE | 23 ( 0.0) | 52 ( 0.0) | ||||
| OTROS DELITOS LEY ORGÁNICA DE INVESTIGACIONES | 3 ( 0.0) | 42 ( 0.0) | ||||
| OTROS DELITOS LY GRAL BANCOS.ART 110,141,142,157,159 DFL 252 | 12 ( 0.0) | 11 ( 0.0) | ||||
| OTROS DELITOS QUE AFECTAN DºS GARANTIDOS POR LA CONSTITUCIÓN | 3 ( 0.0) | 7 ( 0.0) | ||||
| OTROS ESTRAGOS | 22 ( 0.0) | 21 ( 0.0) | ||||
| OTROS HECHOS | 4602 ( 2.4) | 3734 ( 1.0) | ||||
| OTROS LEY 18.314 | 0 ( 0.0) | 1 ( 0.0) | ||||
| PARRICIDIO.ART. 390 inc.1° | 10 ( 0.0) | 37 ( 0.0) | ||||
| PERRO POTENCIALMENTE PELIGROSO NO INSCRITO ART 16 LEY 21.020 | 3 ( 0.0) | 0 ( 0.0) | ||||
| PORTAR ELEMENTOS CONOCIDAMENTE DESTINADOS A COMETER DELITO D | 18 ( 0.0) | 1124 ( 0.3) | ||||
| PORTE ARMA GUERRA, QUÍMICA,BIOLÓGICA,NUCLEAR.ART. 14 INC. 2 | 0 ( 0.0) | 1 ( 0.0) | ||||
| PORTE DE ARMA CORTANTE O PUNZANTE (288 bis) | 150 ( 0.1) | 8293 ( 2.2) | ||||
| PORTE DE ARMA PROHIBIDA (ART. 14 INC. 1°) | 7 ( 0.0) | 125 ( 0.0) | ||||
| PORTE ILEGAL DE ARMA DE FUEGO, MUNIC.Y OTR.SUJETAS A CONTROL | 78 ( 0.0) | 989 ( 0.3) | ||||
| POSESIÓN, TENENCIA ARMA GUERRA, QUÍM, BIOL, OTRA.ART 13 INC1 | 0 ( 0.0) | 3 ( 0.0) | ||||
| POSESIÓN, TENENCIA, PORTE ARMAS ART 9 INC 1 LEY 17779 | 15 ( 0.0) | 490 ( 0.1) | ||||
| POSESIÓN, TENENCIA, PORTE DE MUNIC Y SUST QUÍM ART 9 INC 2 | 6 ( 0.0) | 128 ( 0.0) | ||||
| PRES. PERITOS, TEST O INTÉR.QUE FALTAREN A VERD.O DOC.FALSOS | 0 ( 0.0) | 4 ( 0.0) | ||||
| PRESUNTA DESGRACIA | 19510 ( 10.2) | 61 ( 0.0) | ||||
| PRESUNTA DESGRACIA INFANTIL | 1297 ( 0.7) | 36 ( 0.0) | ||||
| PREVARICACIÓN DEL ABOGADO Y PROCURADOR ARTS. 231 y 232. | 4 ( 0.0) | 6 ( 0.0) | ||||
| PREVARICACIÓN JUDICIAL Y ADMINISTRATIVA ART. 223 AL 229. | 1 ( 0.0) | 1 ( 0.0) | ||||
| PRODUCCIÓN MATERIAL PORNOGRÁFICO UTILIZANDO MENOR.DE 18 AÑOS | 6 ( 0.0) | 7 ( 0.0) | ||||
| PRODUCCIÓN Y TRÁFICO POR DESVÍO DE PRECURS. ART.2 LEY 20000 | 0 ( 0.0) | 1 ( 0.0) | ||||
| PROMOVER O FACILITAR ENTRADA O SALIDA DEL PAIS PARA PROSTIT | 0 ( 0.0) | 1 ( 0.0) | ||||
| PROMOVER O FACILITAR PROSTITUCIÓN DE MENORES . ART. 367. | 76 ( 0.0) | 48 ( 0.0) | ||||
| QUEBRANTAMIENTO. ART. 90 | 4 ( 0.0) | 536 ( 0.1) | ||||
| RECEPTACIÓN ADUANERA. INFRAC ORDZA ADUANAS ART 182 LEY 20780 | 0 ( 0.0) | 10 ( 0.0) | ||||
| RECEPTACIÓN. ART. 456 BIS A | 1005 ( 0.5) | 8140 ( 2.2) | ||||
| RECONOCIMIENTO MALICIOSO DE POSESIÓN REG. ART.9 DEC LEY 2695 | 2 ( 0.0) | 2 ( 0.0) | ||||
| REMISOS (RECLUTAMIENTO) DL 2306. Art. 73 | 0 ( 0.0) | 96 ( 0.0) | ||||
| RIÑA PÚBLICA 496 Nº 10 CÓDIGO PENAL | 741 ( 0.4) | 5756 ( 1.5) | ||||
| ROBO CON FUERZA DE CAJEROS AUTOMATICOS | 0 ( 0.0) | 7 ( 0.0) | ||||
| ROBO CON HOMICIDIO | 10 ( 0.0) | 25 ( 0.0) | ||||
| ROBO CON INTIMIDACIÓN . ART. 433, 436 INC 1º 438. | 8790 ( 4.6) | 4471 ( 1.2) | ||||
| ROBO CON LESIONES GRAVES GRAVISIMAS ART. 433 Nº 2 | 0 ( 0.0) | 1 ( 0.0) | ||||
| ROBO CON RET. DE VICTIMAS O LESIONES GRAVES ART. 433 Nº 3 | 11 ( 0.0) | 3 ( 0.0) | ||||
| ROBO CON RETENCIÓN DE VÍCTIMAS O CON LESIONES GRAVES | 34 ( 0.0) | 15 ( 0.0) | ||||
| ROBO CON VIOLACIÓN. ART. 433 Nº1. | 59 ( 0.0) | 20 ( 0.0) | ||||
| ROBO CON VIOLENCIA. ART.436 inc. 1º 433, 438, 439. | 7380 ( 3.9) | 3514 ( 0.9) | ||||
| ROBO DE VEHICULO MOTORIZADO ART. 443 INC. 2 | 2515 ( 1.3) | 434 ( 0.1) | ||||
| ROBO EN BIENES NAC. DE USO PÚB. O SITIOS NO DESTINADOS A HAB | 3461 ( 1.8) | 3513 ( 0.9) | ||||
| ROBO EN LUGAR HABITADO O DESTINADO A LA HABITACIÓN. ART. 440 | 4733 ( 2.5) | 6150 ( 1.6) | ||||
| ROBO EN LUGAR NO HABITADO. ART. 442. | 2072 ( 1.1) | 7247 ( 1.9) | ||||
| ROBO O HURTO DE MATERIAL DE GUERRA | 0 ( 0.0) | 2 ( 0.0) | ||||
| ROBO POR SORPRESA. ART. 436 INC. 2° | 3755 ( 2.0) | 3124 ( 0.8) | ||||
| ROTURA DE SELLOS.ARTS. 270 y 271. | 0 ( 0.0) | 95 ( 0.0) | ||||
| RUIDOS MOLESTOS 495 Nº 1 CÓDIGO PENAL | 7 ( 0.0) | 91 ( 0.0) | ||||
| SABOTAJE INFORMÁTICO. ARTS. 1 Y 3 LEY 19.223. | 26 ( 0.0) | 38 ( 0.0) | ||||
| SECUESTRO CON HOMICIDIO, VIOLACIÓN O LES. ART.141 INC. FINAL | 29 ( 0.0) | 4 ( 0.0) | ||||
| SECUESTRO CON LESIONES | 5 ( 0.0) | 0 ( 0.0) | ||||
| SECUESTRO CON VIOLACIÓN | 3 ( 0.0) | 0 ( 0.0) | ||||
| SECUESTRO. ART. 141 | 265 ( 0.1) | 58 ( 0.0) | ||||
| SOBORNO DE FUNC PBCO EXTRANJ PERSONA NATURAL ART. 251 BIS | 0 ( 0.0) | 2 ( 0.0) | ||||
| SODOMIA. ART. 365. | 4 ( 0.0) | 0 ( 0.0) | ||||
| SUMINISTRO DE HIDROCARBUROS AROMÁTICOS A MEN.ART.5 LEY 20000 | 1 ( 0.0) | 0 ( 0.0) | ||||
| SUMINISTRO INDEBIDO DE DROGAS. ART. 7 LEY Nº 20.000. | 0 ( 0.0) | 2 ( 0.0) | ||||
| SUSTRACCIÓN DE MENORES. ART. 142 | 9 ( 0.0) | 60 ( 0.0) | ||||
| TACHA FALSA DE FIRMA AUTÉNTICA. ART. 43. DFL 707 | 1 ( 0.0) | 0 ( 0.0) | ||||
| TENENCIA DE ARMAS PROHIBIDAS ART. 13 | 4 ( 0.0) | 46 ( 0.0) | ||||
| TOLERANCIA AL TRÁFICO O CONSUMO DE DROGAS. ART. 12 | 0 ( 0.0) | 2 ( 0.0) | ||||
| TORTURAS COMETIDAS POR FUNCIONARIOS PÚBLICOS (150 A INC 1) | 145 ( 0.1) | 3 ( 0.0) | ||||
| TORTURAS POR PARTICULARES AGENTES DEL ESTADO (150 A INC. 2) | 13 ( 0.0) | 2 ( 0.0) | ||||
| TRÁFICO DE ARMAS (ART. 10) | 0 ( 0.0) | 5 ( 0.0) | ||||
| TRAFICO DE ESPECIES VEGETALES. ART. 10 LEY 20.000 | 0 ( 0.0) | 10 ( 0.0) | ||||
| TRAFICO DE MIGRANTES 411 BIS INCISO 1, 2 Y 3 | 1 ( 0.0) | 0 ( 0.0) | ||||
| TRÁFICO DE RESIDUOS PELIGROSOS (LEY 20.920) | 6 ( 0.0) | 0 ( 0.0) | ||||
| TRÁFICO ILÍCITO DE DROGAS ART. 3 LEY Nº 20.000. | 19 ( 0.0) | 1855 ( 0.5) | ||||
| TRANSP.O DISTRIB.DE GAS E INST.CLAND. ART.53/54 DFL 323,1931 | 1 ( 0.0) | 0 ( 0.0) | ||||
| TRANSPRTE DESECH. VERT. CLAND 192 BIS, INC. 6 LEY 20.879 | 0 ( 0.0) | 1 ( 0.0) | ||||
| TRATA DE PERSONAS CON FINES DE PROSTITUCIÓN. ART. 367 BIS. | 12 ( 0.0) | 0 ( 0.0) | ||||
| TRATA DE PERSONAS PARA LA EXPLOTACIÓN SEXUAL ART 411 QUATER | 1 ( 0.0) | 0 ( 0.0) | ||||
| TRATA PARA TRABAJOS FORZADOS Y OTROS ART411 QUATER INC1° | 1 ( 0.0) | 0 ( 0.0) | ||||
| TRATOS DEGRADANTES A PERSONAS VULNERABLES. ART. 403 TER. | 4 ( 0.0) | 32 ( 0.0) | ||||
| ULTRAJE PÚBLICO A LAS BUENAS COSTUMBRES POR MED.COM.SOCIAL | 11 ( 0.0) | 21 ( 0.0) | ||||
| ULTRAJE PÚBLICO A LAS BUENAS COSTUMBRES. ART. 373. | 23 ( 0.0) | 102 ( 0.0) | ||||
| USO FRAUDULENTO DE TARJETAS O MEDIOS DE PAGO. LEY 20.009 | 1331 ( 0.7) | 416 ( 0.1) | ||||
| USO ILICITO FUEGO ART 18,22,22BIS 22 terC18S 4363 LEYBOSQUES | 2 ( 0.0) | 4 ( 0.0) | ||||
| USO UNIFORME O INSIGNIAS FFAA O CARABINEROS DE CHILE | 0 ( 0.0) | 2 ( 0.0) | ||||
| USO,FACILITACIÓN O TRANSPORTE DE HILO CURADO. | 0 ( 0.0) | 4 ( 0.0) | ||||
| USURA. ART. 472. | 2 ( 0.0) | 0 ( 0.0) | ||||
| USURPACION DE AGUAS. ART. 459. | 3 ( 0.0) | 10 ( 0.0) | ||||
| USURPACIÓN DE ATRIBUCIONES DE EMPLEADOS PÚB. Y JUDICIALES | 9 ( 0.0) | 33 ( 0.0) | ||||
| USURPACIÓN DE ESTADO CIVIL. ART. 354. | 0 ( 0.0) | 1 ( 0.0) | ||||
| USURPACIÓN DE NOMBRE. ART. 214. | 247 ( 0.1) | 741 ( 0.2) | ||||
| USURPACIÓN DE PROPIEDAD, DESCUBRIMIENTO O PROD. ART.158 Nº5. | 41 ( 0.0) | 155 ( 0.0) | ||||
| USURPACIÓN NO VIOLENTA. ART. 458. | 57 ( 0.0) | 230 ( 0.1) | ||||
| USURPACIÓN VIOLENTA. ART. 457 INCISO PRIMERO. | 16 ( 0.0) | 36 ( 0.0) | ||||
| VENTA ILICITA OBRAS PROTEGIDAS LEY PROP INTELECT ART. 81 | 1 ( 0.0) | 177 ( 0.0) | ||||
| VIOLACIÓN | 1 ( 0.0) | 3 ( 0.0) | ||||
| VIOLACIÓN CON HOMICIDIO O FEMICIDIO ART. 372 BIS. | 0 ( 0.0) | 1 ( 0.0) | ||||
| VIOLACIÓN DE MAYOR DE 14 AÑOS. ART. 361. | 1203 ( 0.6) | 462 ( 0.1) | ||||
| VIOLACIÓN DE MENOR DE 14 AÑOS. ART. 362. | 133 ( 0.1) | 308 ( 0.1) | ||||
| VIOLACIÓN DE MORADA. ART. 144 | 1157 ( 0.6) | 3923 ( 1.0) | ||||
| VIOLACIÓN DE SECRETOS. ART. 246. | 0 ( 0.0) | 1 ( 0.0) | ||||
| VIOLENCIA EN LOS ESTADIOS ART 6,6A-B-D-E-F-G-H LEY 19.327 | 2 ( 0.0) | 59 ( 0.0) | ||||
| familia_delito (%) | CUASIDELITOS | 2026 ( 1.1) | 1811 ( 0.5) | <0.001 | 1.114 | |
| DELITOS CONTRA LA FE PÚBLICA | 2518 ( 1.3) | 13467 ( 3.6) | ||||
| DELITOS CONTRA LA LIBERTAD E INTIMIDAD DE LAS PERSONAS | 32955 ( 17.2) | 66005 ( 17.5) | ||||
| DELITOS CONTRA LEYES DE PROPIEDAD INTELECTUAL E INDUSTRIAL | 7 ( 0.0) | 1466 ( 0.4) | ||||
| DELITOS DE JUSTICIA MILITAR | 10 ( 0.0) | 37 ( 0.0) | ||||
| DELITOS DE LEYES ESPECIALES | 2728 ( 1.4) | 10502 ( 2.8) | ||||
| DELITOS DE TORTURA, MALOS TRATOS, GENOCIDIO Y LESA HUMANIDAD | 445 ( 0.2) | 8 ( 0.0) | ||||
| DELITOS ECONÓMICOS Y TRIBUTARIOS | 4816 ( 2.5) | 9455 ( 2.5) | ||||
| DELITOS FUNCIONARIOS | 394 ( 0.2) | 344 ( 0.1) | ||||
| DELITOS LEY DE DROGAS | 87 ( 0.0) | 9489 ( 2.5) | ||||
| DELITOS LEY DE TRÁNSITO | 1597 ( 0.8) | 14504 ( 3.8) | ||||
| DELITOS SEXUALES | 2621 ( 1.4) | 3239 ( 0.9) | ||||
| FALTAS | 4171 ( 2.2) | 76683 ( 20.3) | ||||
| HECHOS DE RELEVANCIA CRIMINAL | 28175 ( 14.7) | 4090 ( 1.1) | ||||
| HOMICIDIOS | 518 ( 0.3) | 589 ( 0.2) | ||||
| HURTOS | 9956 ( 5.2) | 42132 ( 11.2) | ||||
| LESIONES | 57764 ( 30.2) | 61071 ( 16.2) | ||||
| OTROS DELITOS | 369 ( 0.2) | 12227 ( 3.2) | ||||
| OTROS DELITOS CONTRA LA PROPIEDAD | 7188 ( 3.8) | 21489 ( 5.7) | ||||
| ROBOS | 20039 ( 10.5) | 11173 ( 3.0) | ||||
| ROBOS NO VIOLENTOS | 12781 ( 6.7) | 17351 ( 4.6) | ||||
| relacion_vifsaf (%) | NO | 154405 ( 80.8) | 298055 ( 79.0) | <0.001 | 0.043 | |
| SI | 36760 ( 19.2) | 79077 ( 21.0) | ||||
| gls_parentesco (%) | ABUELO(A) | 31 ( 0.0) | 716 ( 0.2) | <0.001 | 0.230 | |
| ADOPTADO | 17 ( 0.0) | 24 ( 0.0) | ||||
| ADOPTANTE | 1 ( 0.0) | 4 ( 0.0) | ||||
| CONVIVIENTE | 13227 ( 6.9) | 17234 ( 4.6) | ||||
| CÓNYUGE | 3983 ( 2.1) | 11121 ( 2.9) | ||||
| CUÑADO(A) | 633 ( 0.3) | 1068 ( 0.3) | ||||
| EX CONVIVIENTE | 8104 ( 4.2) | 13275 ( 3.5) | ||||
| EX CONYUGE | 358 ( 0.2) | 1059 ( 0.3) | ||||
| HERMANO(A) | 3030 ( 1.6) | 6806 ( 1.8) | ||||
| HIJO(A) | 1619 ( 0.8) | 7512 ( 2.0) | ||||
| MADRASTRA | 22 ( 0.0) | 81 ( 0.0) | ||||
| MADRE | 951 ( 0.5) | 7461 ( 2.0) | ||||
| MADRE HIJO COMUN | 1295 ( 0.7) | 2590 ( 0.7) | ||||
| MENOR DE EDAD (bajo cuidado) | 8 ( 0.0) | 36 ( 0.0) | ||||
| MINUSVALIDO (bajo cuidado) | 1 ( 0.0) | 2 ( 0.0) | ||||
| NIETO | 38 ( 0.0) | 314 ( 0.1) | ||||
| No Definido | 40 ( 0.0) | 78 ( 0.0) | ||||
| NUERA | 91 ( 0.0) | 168 ( 0.0) | ||||
| PADRASTRO | 217 ( 0.1) | 545 ( 0.1) | ||||
| PADRE | 634 ( 0.3) | 3630 ( 1.0) | ||||
| PADRE HIJO COMUN | 1220 ( 0.6) | 2364 ( 0.6) | ||||
| SOBRINO(A) | 439 ( 0.2) | 1012 ( 0.3) | ||||
| SUEGRO(A) | 219 ( 0.1) | 647 ( 0.2) | ||||
| TIO(A) | 450 ( 0.2) | 1059 ( 0.3) | ||||
| YERNO | 132 ( 0.1) | 271 ( 0.1) | ||||
| 154405 ( 80.8) | 298055 ( 79.0) | |||||
| gls_mottermino (%) | Acoge req. proced monit y proposic multa | 5722 ( 3.0) | 51338 ( 13.6) | <0.001 | 0.968 | |
| Acuerdo Reparatorio (Art. 241 y 242) | 2702 ( 1.4) | 9369 ( 2.5) | ||||
| Agrupado | 11361 ( 5.9) | 16920 ( 4.5) | ||||
| Anulación de ingreso error de digitación | 354 ( 0.2) | 1221 ( 0.3) | ||||
| Anulación ingreso por delito acción priv | 14 ( 0.0) | 19 ( 0.0) | ||||
| Archivo Provisional | 87494 ( 45.8) | 62180 ( 16.5) | ||||
| Decisión de no perseverar en el proced | 9942 ( 5.2) | 23263 ( 6.2) | ||||
| Incomp materias correspond sistema antig | 45 ( 0.0) | 42 ( 0.0) | ||||
| Incomp tratarse materias conoc otro trib | 923 ( 0.5) | 1423 ( 0.4) | ||||
| Negativa investig caso autodenuncia 179 | 0 ( 0.0) | 1 ( 0.0) | ||||
| No Inicio Investigación | 26158 ( 13.7) | 16093 ( 4.3) | ||||
| Otras causales de término | 275 ( 0.1) | 980 ( 0.3) | ||||
| Principio de Oportunidad | 12130 ( 6.3) | 33072 ( 8.8) | ||||
| Rechazo querella de capítulos (art. 429) | 0 ( 0.0) | 3 ( 0.0) | ||||
| Rechazo req. Med. Seg. Opuest qte 462 | 0 ( 0.0) | 2 ( 0.0) | ||||
| S Def (no subs oport vicios form art270) | 14 ( 0.0) | 56 ( 0.0) | ||||
| S Definit (vencim plazo suspens art 240) | 9547 ( 5.0) | 35081 ( 9.3) | ||||
| S. Definit (en casos del art. 465) | 1 ( 0.0) | 2 ( 0.0) | ||||
| S. Definit (exc previo y espec pron 271) | 8 ( 0.0) | 14 ( 0.0) | ||||
| S. Definit (susp imposic de la cond 398) | 12 ( 0.0) | 32 ( 0.0) | ||||
| Sentencia definitiva absolutoria | 2898 ( 1.5) | 8954 ( 2.4) | ||||
| Sentencia definitiva condenatoria | 13068 ( 6.8) | 87062 ( 23.1) | ||||
| Sobres Definit (en casos del art 247) | 2 ( 0.0) | 10 ( 0.0) | ||||
| Sobres Definitivo (causales del art 250) | 6660 ( 3.5) | 21474 ( 5.7) | ||||
| Término requerimiento internacional | 0 ( 0.0) | 3 ( 0.0) | ||||
| 1835 ( 1.0) | 8518 ( 2.3) | |||||
| gls_motsuspension (%) | ACUERDO REPARATORIO | 3224 ( 1.7) | 12232 ( 3.2) | <0.001 | 0.237 | |
| SOBRESEIMIENTO TEMPORAL | 495 ( 0.3) | 1780 ( 0.5) | ||||
| SUSPENSION CONDICIONAL DEL PROCEDIMIENTO | 11826 ( 6.2) | 43805 ( 11.6) | ||||
| SUSPENSION PROCED. ART. 10 | 0 ( 0.0) | 10 ( 0.0) | ||||
| SUSPENSION PROCED. ART. 458 | 138 ( 0.1) | 1581 ( 0.4) | ||||
| 175482 ( 91.8) | 317724 ( 84.2) | |||||
| gls_proctermino (%) | ABREVIADO | 3603 ( 1.9) | 17857 ( 4.7) | <0.001 | 0.829 | |
| MONITORIO | 5729 ( 3.0) | 51384 ( 13.6) | ||||
| ORDINARIO | 26580 ( 13.9) | 80234 ( 21.3) | ||||
| RECLAMO | 0 ( 0.0) | 1 ( 0.0) | ||||
| REQUERIMIENTO INTERNACIONAL | 0 ( 0.0) | 3 ( 0.0) | ||||
| SIMPLIFICADO | 14807 ( 7.7) | 87775 ( 23.3) | ||||
| SIN PROCEDIMIENTO | 140446 ( 73.5) | 139878 ( 37.1) | ||||
| gls_tipo_imputado (%) | DENUNCIADO | 5384 ( 2.8) | 23013 ( 6.1) | <0.001 | 0.165 | |
| IMPUTADO | 185542 ( 97.1) | 353445 ( 93.7) | ||||
| INVESTIGADO | 10 ( 0.0) | 17 ( 0.0) | ||||
| QUERELLADO | 95 ( 0.0) | 467 ( 0.1) | ||||
| SOSPECHOSO | 45 ( 0.0) | 189 ( 0.1) | ||||
| TESTIGO | 89 ( 0.0) | 1 ( 0.0) | ||||
| iddelito (mean (SD)) | 13621186.67 (4161804.95) | 13344286.73 (4204829.19) | <0.001 | 0.066 | ||
| lugar_ocurrencia (%) | BIENES NACIONALES USO PUBLICO | 79544 ( 41.6) | 141594 ( 37.5) | <0.001 | 0.453 | |
| ESTABLECIMIENTO COMERCIAL | 11902 ( 6.2) | 79365 ( 21.0) | ||||
| ESTABLECIMIENTO DE DIVERSION | 805 ( 0.4) | 1048 ( 0.3) | ||||
| ESTABLECIMIENTO EDUCACIONAL | 1282 ( 0.7) | 2221 ( 0.6) | ||||
| ESTABLECIMIENTO PRODUCTIVO | 3238 ( 1.7) | 4966 ( 1.3) | ||||
| LUGAR HABITADO O DESTINADO A LA HABITACION Y SUS DEPENDENCIAS | 83821 ( 43.8) | 129359 ( 34.3) | ||||
| ORGANIZACION DE SEGURIDAD / JUSTICIA | 4372 ( 2.3) | 7874 ( 2.1) | ||||
| ORGANIZACION PUBLICA Y/O SOCIAL | 3070 ( 1.6) | 4306 ( 1.1) | ||||
| OTROS LUGARES NO CLASIFICADOS | 2308 ( 1.2) | 3356 ( 0.9) | ||||
| 823 ( 0.4) | 3043 ( 0.8) | |||||
| gls_sitiosuceso (%) | ANEXOS VIVIENDA | 584 ( 0.3) | 1336 ( 0.4) | <0.001 | 0.510 | |
| AUTOSERVICIO | 2504 ( 1.3) | 34206 ( 9.1) | ||||
| BOMBEROS | 8 ( 0.0) | 13 ( 0.0) | ||||
| CAJERO AUTOMATICO | 323 ( 0.2) | 213 ( 0.1) | ||||
| CARRETERAS Y CAMINOS | 3371 ( 1.8) | 9114 ( 2.4) | ||||
| CEMENTERIOS | 82 ( 0.0) | 144 ( 0.0) | ||||
| CENTRALES DE APUESTAS | 27 ( 0.0) | 45 ( 0.0) | ||||
| CENTRO ESPECTACULOS | 99 ( 0.1) | 192 ( 0.1) | ||||
| CENTRO RECREATIVO | 204 ( 0.1) | 322 ( 0.1) | ||||
| CENTRO SOCIAL | 413 ( 0.2) | 460 ( 0.1) | ||||
| CENTROS CULTURALES | 19 ( 0.0) | 47 ( 0.0) | ||||
| CENTROS DE ASISTENCIA MEDICA | 1731 ( 0.9) | 1895 ( 0.5) | ||||
| CENTROS DE CULTO | 48 ( 0.0) | 193 ( 0.1) | ||||
| CENTROS DE JUEGOS | 87 ( 0.0) | 113 ( 0.0) | ||||
| CERRO | 35 ( 0.0) | 100 ( 0.0) | ||||
| COMERCIO MENOR | 1339 ( 0.7) | 6909 ( 1.8) | ||||
| CUERPO DE AGUA | 6 ( 0.0) | 21 ( 0.0) | ||||
| CURSO DE AGUA | 11 ( 0.0) | 14 ( 0.0) | ||||
| DEFENSORIA | 2 ( 0.0) | 8 ( 0.0) | ||||
| DISCOTEQUE / PUB | 535 ( 0.3) | 640 ( 0.2) | ||||
| EMPRESA / COMPAÑIA | 1536 ( 0.8) | 2642 ( 0.7) | ||||
| ESCOLAR | 1114 ( 0.6) | 1786 ( 0.5) | ||||
| ESTABLECIMIENTO FINANCIERO | 576 ( 0.3) | 1421 ( 0.4) | ||||
| ESTABLECIMIENTOS DE ALIMENTACION | 911 ( 0.5) | 3198 ( 0.8) | ||||
| ESTACIONAMIENTO ABIERTO | 192 ( 0.1) | 269 ( 0.1) | ||||
| ESTACIONAMIENTO CERRADO | 146 ( 0.1) | 136 ( 0.0) | ||||
| EXPENDIO DE BEBIDAS ALCOHOLICAS | 127 ( 0.1) | 335 ( 0.1) | ||||
| FERIAS LIBRES | 153 ( 0.1) | 352 ( 0.1) | ||||
| FISCALIA | 19 ( 0.0) | 47 ( 0.0) | ||||
| INDUSTRIA | 1436 ( 0.8) | 1838 ( 0.5) | ||||
| MAR | 44 ( 0.0) | 71 ( 0.0) | ||||
| MINAS Y PIQUES MINEROS | 10 ( 0.0) | 28 ( 0.0) | ||||
| PARQUE / PLAZA | 446 ( 0.2) | 774 ( 0.2) | ||||
| PLAYA | 142 ( 0.1) | 224 ( 0.1) | ||||
| PRE-ESCOLAR | 45 ( 0.0) | 220 ( 0.1) | ||||
| PREDIO RURAL | 1324 ( 0.7) | 2078 ( 0.6) | ||||
| PROSTIBULOS | 57 ( 0.0) | 57 ( 0.0) | ||||
| PUENTES | 15 ( 0.0) | 15 ( 0.0) | ||||
| RECINTOS DE GENDARMERIA | 2797 ( 1.5) | 5730 ( 1.5) | ||||
| RECINTOS FFAA | 62 ( 0.0) | 162 ( 0.0) | ||||
| RECINTOS POLICIALES | 1081 ( 0.6) | 1106 ( 0.3) | ||||
| SERVICENTRO | 1153 ( 0.6) | 1374 ( 0.4) | ||||
| SERVICIOS PUBLICOS | 652 ( 0.3) | 1371 ( 0.4) | ||||
| SITIO ERIAZO PARTICULAR | 832 ( 0.4) | 1033 ( 0.3) | ||||
| SITIO ERIAZO PUBLICO | 129 ( 0.1) | 144 ( 0.0) | ||||
| SUPERIOR | 120 ( 0.1) | 209 ( 0.1) | ||||
| TALLER | 245 ( 0.1) | 449 ( 0.1) | ||||
| TERMINALES DE TRANSPORTES | 878 ( 0.5) | 1223 ( 0.3) | ||||
| TIENDA COMERCIAL | 4081 ( 2.1) | 30374 ( 8.1) | ||||
| TRIBUNALES | 401 ( 0.2) | 805 ( 0.2) | ||||
| VEHICULO EN VIA PUBLICA | 2686 ( 1.4) | 3797 ( 1.0) | ||||
| VIA PUBLICA URBANA | 72118 ( 37.7) | 126225 ( 33.5) | ||||
| VIVIENDA | 82311 ( 43.1) | 126757 ( 33.6) | ||||
| VIVIENDAS TRANSITORIAS | 872 ( 0.5) | 1120 ( 0.3) | ||||
| 1026 ( 0.5) | 3777 ( 1.0) | |||||
| gls_comuna (%) | AISEN | 491 ( 0.3) | 823 ( 0.2) | <0.001 | 0.205 | |
| ALGARROBO | 139 ( 0.1) | 304 ( 0.1) | ||||
| ALHUE | 31 ( 0.0) | 43 ( 0.0) | ||||
| ALTO BIO BIO | 17 ( 0.0) | 13 ( 0.0) | ||||
| ALTO DEL CARMEN | 19 ( 0.0) | 28 ( 0.0) | ||||
| ALTO HOSPICIO | 1639 ( 0.9) | 4089 ( 1.1) | ||||
| ANCUD | 192 ( 0.1) | 282 ( 0.1) | ||||
| ANDACOLLO | 155 ( 0.1) | 320 ( 0.1) | ||||
| ANGOL | 417 ( 0.2) | 770 ( 0.2) | ||||
| ANTARTICA | 0 ( 0.0) | 2 ( 0.0) | ||||
| ANTOFAGASTA | 3869 ( 2.0) | 10379 ( 2.8) | ||||
| ANTUCO | 11 ( 0.0) | 18 ( 0.0) | ||||
| ARAUCO | 377 ( 0.2) | 908 ( 0.2) | ||||
| ARICA | 4532 ( 2.4) | 9338 ( 2.5) | ||||
| BUIN | 844 ( 0.4) | 1664 ( 0.4) | ||||
| BULNES | 66 ( 0.0) | 76 ( 0.0) | ||||
| CABILDO | 76 ( 0.0) | 153 ( 0.0) | ||||
| CABRERO | 58 ( 0.0) | 233 ( 0.1) | ||||
| CALAMA | 2256 ( 1.2) | 6217 ( 1.6) | ||||
| CALBUCO | 234 ( 0.1) | 440 ( 0.1) | ||||
| CALDERA | 764 ( 0.4) | 1580 ( 0.4) | ||||
| CALERA DE TANGO | 301 ( 0.2) | 490 ( 0.1) | ||||
| CALLE LARGA | 44 ( 0.0) | 91 ( 0.0) | ||||
| CAMARONES | 3 ( 0.0) | 27 ( 0.0) | ||||
| CAMINA | 4 ( 0.0) | 5 ( 0.0) | ||||
| CANELA | 22 ( 0.0) | 25 ( 0.0) | ||||
| CAÑETE | 312 ( 0.2) | 814 ( 0.2) | ||||
| CARAHUE | 128 ( 0.1) | 247 ( 0.1) | ||||
| CARTAGENA | 518 ( 0.3) | 876 ( 0.2) | ||||
| CASABLANCA | 447 ( 0.2) | 985 ( 0.3) | ||||
| CASTRO | 461 ( 0.2) | 1038 ( 0.3) | ||||
| CATEMU | 79 ( 0.0) | 263 ( 0.1) | ||||
| CAUQUENES | 434 ( 0.2) | 737 ( 0.2) | ||||
| CERRILLOS | 1204 ( 0.6) | 2280 ( 0.6) | ||||
| CERRO NAVIA | 1564 ( 0.8) | 3064 ( 0.8) | ||||
| CHAITEN | 13 ( 0.0) | 24 ( 0.0) | ||||
| CHANCO | 19 ( 0.0) | 55 ( 0.0) | ||||
| CHAÑARAL | 718 ( 0.4) | 1520 ( 0.4) | ||||
| CHEPICA | 72 ( 0.0) | 211 ( 0.1) | ||||
| CHIGUAYANTE | 538 ( 0.3) | 758 ( 0.2) | ||||
| CHILE CHICO | 167 ( 0.1) | 259 ( 0.1) | ||||
| CHILLAN | 1182 ( 0.6) | 2582 ( 0.7) | ||||
| CHILLAN VIEJO | 116 ( 0.1) | 186 ( 0.0) | ||||
| CHIMBARONGO | 167 ( 0.1) | 456 ( 0.1) | ||||
| CHOL CHOL | 23 ( 0.0) | 47 ( 0.0) | ||||
| CHONCHI | 45 ( 0.0) | 107 ( 0.0) | ||||
| CISNES | 190 ( 0.1) | 270 ( 0.1) | ||||
| COBQUECURA | 4 ( 0.0) | 23 ( 0.0) | ||||
| COCHAMO | 3 ( 0.0) | 2 ( 0.0) | ||||
| COCHRANE | 107 ( 0.1) | 181 ( 0.0) | ||||
| CODEGUA | 78 ( 0.0) | 148 ( 0.0) | ||||
| COELEMU | 38 ( 0.0) | 119 ( 0.0) | ||||
| COIHUECO | 27 ( 0.0) | 25 ( 0.0) | ||||
| COINCO | 16 ( 0.0) | 47 ( 0.0) | ||||
| COLBUN | 34 ( 0.0) | 68 ( 0.0) | ||||
| COLCHANE | 1 ( 0.0) | 8 ( 0.0) | ||||
| COLINA | 2570 ( 1.3) | 5299 ( 1.4) | ||||
| COLLIPULLI | 408 ( 0.2) | 568 ( 0.2) | ||||
| COLTAUCO | 61 ( 0.0) | 148 ( 0.0) | ||||
| COMBARBALA | 40 ( 0.0) | 84 ( 0.0) | ||||
| CONCEPCION | 2400 ( 1.3) | 5177 ( 1.4) | ||||
| CONCHALI | 1533 ( 0.8) | 3404 ( 0.9) | ||||
| CONCON | 437 ( 0.2) | 649 ( 0.2) | ||||
| CONSTITUCION | 505 ( 0.3) | 944 ( 0.3) | ||||
| CONTULMO | 15 ( 0.0) | 24 ( 0.0) | ||||
| COPIAPO | 2136 ( 1.1) | 5431 ( 1.4) | ||||
| COQUIMBO | 2679 ( 1.4) | 4600 ( 1.2) | ||||
| CORONEL | 1396 ( 0.7) | 2779 ( 0.7) | ||||
| CORRAL | 17 ( 0.0) | 9 ( 0.0) | ||||
| COYHAIQUE | 999 ( 0.5) | 1694 ( 0.4) | ||||
| CUNCO | 21 ( 0.0) | 59 ( 0.0) | ||||
| CURACAUTIN | 29 ( 0.0) | 50 ( 0.0) | ||||
| CURACAVI | 547 ( 0.3) | 1071 ( 0.3) | ||||
| CURACO DE VELEZ | 2 ( 0.0) | 6 ( 0.0) | ||||
| CURANILAHUE | 349 ( 0.2) | 820 ( 0.2) | ||||
| CURARREHUE | 44 ( 0.0) | 61 ( 0.0) | ||||
| CUREPTO | 8 ( 0.0) | 30 ( 0.0) | ||||
| CURICO | 2614 ( 1.4) | 6072 ( 1.6) | ||||
| DALCAHUE | 55 ( 0.0) | 136 ( 0.0) | ||||
| DIEGO DE ALMAGRO | 313 ( 0.2) | 717 ( 0.2) | ||||
| DOQIHUE | 100 ( 0.1) | 155 ( 0.0) | ||||
| EL BOSQUE | 2128 ( 1.1) | 3878 ( 1.0) | ||||
| EL CARMEN | 7 ( 0.0) | 33 ( 0.0) | ||||
| EL MONTE | 564 ( 0.3) | 1102 ( 0.3) | ||||
| EL QUISCO | 305 ( 0.2) | 450 ( 0.1) | ||||
| EL TABO | 212 ( 0.1) | 351 ( 0.1) | ||||
| EMPEDRADO | 8 ( 0.0) | 11 ( 0.0) | ||||
| ERCILLA | 25 ( 0.0) | 18 ( 0.0) | ||||
| ESTACION CENTRAL | 2415 ( 1.3) | 4767 ( 1.3) | ||||
| FLORIDA | 41 ( 0.0) | 39 ( 0.0) | ||||
| FREIRE | 46 ( 0.0) | 67 ( 0.0) | ||||
| FREIRINA | 183 ( 0.1) | 227 ( 0.1) | ||||
| FRESIA | 9 ( 0.0) | 44 ( 0.0) | ||||
| FRUTILLAR | 96 ( 0.1) | 233 ( 0.1) | ||||
| FUTALEUFU | 2 ( 0.0) | 28 ( 0.0) | ||||
| FUTRONO | 17 ( 0.0) | 42 ( 0.0) | ||||
| GALVARINO | 16 ( 0.0) | 19 ( 0.0) | ||||
| GENERAL LAGOS | 0 ( 0.0) | 1 ( 0.0) | ||||
| GORBEA | 28 ( 0.0) | 44 ( 0.0) | ||||
| GRANEROS | 579 ( 0.3) | 1230 ( 0.3) | ||||
| GUAITECAS | 11 ( 0.0) | 26 ( 0.0) | ||||
| HIJUELAS | 93 ( 0.0) | 158 ( 0.0) | ||||
| HUALAIHUE | 33 ( 0.0) | 33 ( 0.0) | ||||
| HUALAQE | 55 ( 0.0) | 155 ( 0.0) | ||||
| HUALPEN | 585 ( 0.3) | 1329 ( 0.4) | ||||
| HUALQUI | 105 ( 0.1) | 93 ( 0.0) | ||||
| HUARA | 41 ( 0.0) | 60 ( 0.0) | ||||
| HUASCO | 139 ( 0.1) | 234 ( 0.1) | ||||
| HUECHURABA | 864 ( 0.5) | 2107 ( 0.6) | ||||
| ILLAPEL | 78 ( 0.0) | 190 ( 0.1) | ||||
| INDEPENDENCIA | 1163 ( 0.6) | 1847 ( 0.5) | ||||
| IQUIQUE | 2493 ( 1.3) | 8204 ( 2.2) | ||||
| ISLA DE MAIPO | 381 ( 0.2) | 754 ( 0.2) | ||||
| ISLA DE PASCUA | 696 ( 0.4) | 1197 ( 0.3) | ||||
| JUAN FERNANDEZ | 6 ( 0.0) | 12 ( 0.0) | ||||
| LA CALERA | 370 ( 0.2) | 1125 ( 0.3) | ||||
| LA CISTERNA | 1437 ( 0.8) | 2270 ( 0.6) | ||||
| LA CRUZ | 63 ( 0.0) | 176 ( 0.0) | ||||
| LA ESTRELLA | 27 ( 0.0) | 31 ( 0.0) | ||||
| LA FLORIDA | 3829 ( 2.0) | 8196 ( 2.2) | ||||
| LA GRANJA | 1973 ( 1.0) | 2947 ( 0.8) | ||||
| LA HIGUERA | 31 ( 0.0) | 73 ( 0.0) | ||||
| LA LIGUA | 164 ( 0.1) | 572 ( 0.2) | ||||
| LA PINTANA | 3811 ( 2.0) | 5391 ( 1.4) | ||||
| LA REINA | 688 ( 0.4) | 1849 ( 0.5) | ||||
| LA SERENA | 2748 ( 1.4) | 5272 ( 1.4) | ||||
| LA UNION | 234 ( 0.1) | 481 ( 0.1) | ||||
| LAGO RANCO | 20 ( 0.0) | 31 ( 0.0) | ||||
| LAGO VERDE | 8 ( 0.0) | 19 ( 0.0) | ||||
| LAGUNA BLANCA | 1 ( 0.0) | 1 ( 0.0) | ||||
| LAJA | 149 ( 0.1) | 269 ( 0.1) | ||||
| LAMPA | 1422 ( 0.7) | 1792 ( 0.5) | ||||
| LANCO | 31 ( 0.0) | 65 ( 0.0) | ||||
| LAS CABRAS | 150 ( 0.1) | 285 ( 0.1) | ||||
| LAS CONDES | 895 ( 0.5) | 3054 ( 0.8) | ||||
| LAUTARO | 66 ( 0.0) | 179 ( 0.0) | ||||
| LEBU | 259 ( 0.1) | 377 ( 0.1) | ||||
| LICANTEN | 63 ( 0.0) | 235 ( 0.1) | ||||
| LIMACHE | 256 ( 0.1) | 622 ( 0.2) | ||||
| LINARES | 736 ( 0.4) | 1577 ( 0.4) | ||||
| LITUECHE | 29 ( 0.0) | 92 ( 0.0) | ||||
| LLAILLAY | 198 ( 0.1) | 507 ( 0.1) | ||||
| LLANQUIHUE | 82 ( 0.0) | 156 ( 0.0) | ||||
| LO BARNECHEA | 904 ( 0.5) | 2124 ( 0.6) | ||||
| LO ESPEJO | 1761 ( 0.9) | 2700 ( 0.7) | ||||
| LO PRADO | 1916 ( 1.0) | 3241 ( 0.9) | ||||
| LOLOL | 8 ( 0.0) | 27 ( 0.0) | ||||
| LONCOCHE | 97 ( 0.1) | 129 ( 0.0) | ||||
| LONGAVI | 76 ( 0.0) | 99 ( 0.0) | ||||
| LONQUIMAY | 10 ( 0.0) | 21 ( 0.0) | ||||
| LOS ALAMOS | 86 ( 0.0) | 148 ( 0.0) | ||||
| LOS ANDES | 702 ( 0.4) | 1705 ( 0.5) | ||||
| LOS ANGELES | 1354 ( 0.7) | 3344 ( 0.9) | ||||
| LOS LAGOS | 37 ( 0.0) | 57 ( 0.0) | ||||
| LOS MUERMOS | 31 ( 0.0) | 30 ( 0.0) | ||||
| LOS SAUCES | 120 ( 0.1) | 212 ( 0.1) | ||||
| LOS VILOS | 449 ( 0.2) | 662 ( 0.2) | ||||
| LOTA | 597 ( 0.3) | 1091 ( 0.3) | ||||
| LUMACO | 28 ( 0.0) | 49 ( 0.0) | ||||
| MACHALI | 346 ( 0.2) | 574 ( 0.2) | ||||
| MACUL | 1498 ( 0.8) | 3039 ( 0.8) | ||||
| MAFIL | 13 ( 0.0) | 21 ( 0.0) | ||||
| MAIPU | 3385 ( 1.8) | 6517 ( 1.7) | ||||
| MALLOA | 39 ( 0.0) | 116 ( 0.0) | ||||
| MARCHIHUE | 34 ( 0.0) | 75 ( 0.0) | ||||
| MARIA ELENA | 52 ( 0.0) | 122 ( 0.0) | ||||
| MARIA PINTO | 184 ( 0.1) | 378 ( 0.1) | ||||
| MARIQUINA | 95 ( 0.0) | 144 ( 0.0) | ||||
| MAULE | 226 ( 0.1) | 363 ( 0.1) | ||||
| MAULLIN | 16 ( 0.0) | 53 ( 0.0) | ||||
| MEJILLONES | 280 ( 0.1) | 323 ( 0.1) | ||||
| MELIPEUCO | 23 ( 0.0) | 34 ( 0.0) | ||||
| MELIPILLA | 3018 ( 1.6) | 5218 ( 1.4) | ||||
| MOLINA | 458 ( 0.2) | 925 ( 0.2) | ||||
| MONTE PATRIA | 152 ( 0.1) | 316 ( 0.1) | ||||
| MOSTAZAL | 288 ( 0.2) | 547 ( 0.1) | ||||
| MULCHEN | 151 ( 0.1) | 348 ( 0.1) | ||||
| NACIMIENTO | 98 ( 0.1) | 319 ( 0.1) | ||||
| NANCAGUA | 162 ( 0.1) | 306 ( 0.1) | ||||
| NATALES | 281 ( 0.1) | 451 ( 0.1) | ||||
| NAVARINO | 7 ( 0.0) | 19 ( 0.0) | ||||
| NAVIDAD | 25 ( 0.0) | 42 ( 0.0) | ||||
| NEGRETE | 24 ( 0.0) | 77 ( 0.0) | ||||
| NINHUE | 7 ( 0.0) | 5 ( 0.0) | ||||
| No Definido | 1 ( 0.0) | 3 ( 0.0) | ||||
| NOGALES | 135 ( 0.1) | 190 ( 0.1) | ||||
| NUEVA IMPERIAL | 240 ( 0.1) | 490 ( 0.1) | ||||
| ÑIQUEN | 13 ( 0.0) | 11 ( 0.0) | ||||
| ÑUÑOA | 1507 ( 0.8) | 3110 ( 0.8) | ||||
| OHIGGINS | 12 ( 0.0) | 19 ( 0.0) | ||||
| OLIVAR | 109 ( 0.1) | 110 ( 0.0) | ||||
| OLLAGUE | 1 ( 0.0) | 2 ( 0.0) | ||||
| OLMUE | 31 ( 0.0) | 54 ( 0.0) | ||||
| OSORNO | 1600 ( 0.8) | 3555 ( 0.9) | ||||
| OVALLE | 1092 ( 0.6) | 2499 ( 0.7) | ||||
| PADRE DE LAS CASAS | 356 ( 0.2) | 407 ( 0.1) | ||||
| PADRE HURTADO | 691 ( 0.4) | 1072 ( 0.3) | ||||
| PAIHUANO | 37 ( 0.0) | 73 ( 0.0) | ||||
| PAILLACO | 56 ( 0.0) | 144 ( 0.0) | ||||
| PAINE | 503 ( 0.3) | 945 ( 0.3) | ||||
| PALENA | 7 ( 0.0) | 13 ( 0.0) | ||||
| PALMILLA | 36 ( 0.0) | 128 ( 0.0) | ||||
| PANGUIPULLI | 172 ( 0.1) | 263 ( 0.1) | ||||
| PANQUEHUE | 37 ( 0.0) | 93 ( 0.0) | ||||
| PAPUDO | 18 ( 0.0) | 26 ( 0.0) | ||||
| PAREDONES | 16 ( 0.0) | 22 ( 0.0) | ||||
| PARRAL | 318 ( 0.2) | 581 ( 0.2) | ||||
| PEDRO AGUIRRE CERDA | 1569 ( 0.8) | 2426 ( 0.6) | ||||
| PELARCO | 56 ( 0.0) | 94 ( 0.0) | ||||
| PELLUHUE | 96 ( 0.1) | 210 ( 0.1) | ||||
| PEMUCO | 14 ( 0.0) | 8 ( 0.0) | ||||
| PENCAHUE | 13 ( 0.0) | 54 ( 0.0) | ||||
| PENCO | 616 ( 0.3) | 943 ( 0.3) | ||||
| PEÑAFLOR | 1097 ( 0.6) | 2319 ( 0.6) | ||||
| PEÑALOLEN | 3234 ( 1.7) | 5238 ( 1.4) | ||||
| PERALILLO | 74 ( 0.0) | 190 ( 0.1) | ||||
| PERQUENCO | 6 ( 0.0) | 19 ( 0.0) | ||||
| PETORCA | 29 ( 0.0) | 75 ( 0.0) | ||||
| PEUMO | 104 ( 0.1) | 307 ( 0.1) | ||||
| PICA | 146 ( 0.1) | 304 ( 0.1) | ||||
| PICHIDEGUA | 70 ( 0.0) | 114 ( 0.0) | ||||
| PICHILEMU | 529 ( 0.3) | 1064 ( 0.3) | ||||
| PINTO | 35 ( 0.0) | 62 ( 0.0) | ||||
| PIRQUE | 233 ( 0.1) | 299 ( 0.1) | ||||
| PITRUFQUEN | 66 ( 0.0) | 88 ( 0.0) | ||||
| PLACILLA | 54 ( 0.0) | 67 ( 0.0) | ||||
| PORTEZUELO | 5 ( 0.0) | 9 ( 0.0) | ||||
| PORVENIR | 24 ( 0.0) | 66 ( 0.0) | ||||
| POZO ALMONTE | 275 ( 0.1) | 735 ( 0.2) | ||||
| PRIMAVERA | 5 ( 0.0) | 3 ( 0.0) | ||||
| PROVIDENCIA | 1227 ( 0.6) | 2798 ( 0.7) | ||||
| PUCHUNCAVI | 295 ( 0.2) | 425 ( 0.1) | ||||
| PUCON | 585 ( 0.3) | 957 ( 0.3) | ||||
| PUDAHUEL | 4073 ( 2.1) | 6455 ( 1.7) | ||||
| PUENTE ALTO | 6026 ( 3.2) | 10901 ( 2.9) | ||||
| PUERTO MONTT | 2145 ( 1.1) | 4462 ( 1.2) | ||||
| PUERTO OCTAY | 47 ( 0.0) | 76 ( 0.0) | ||||
| PUERTO VARAS | 374 ( 0.2) | 778 ( 0.2) | ||||
| PUMANQUE | 6 ( 0.0) | 2 ( 0.0) | ||||
| PUNITAQUI | 52 ( 0.0) | 99 ( 0.0) | ||||
| PUNTA ARENAS | 1643 ( 0.9) | 2690 ( 0.7) | ||||
| PUQUELDON | 2 ( 0.0) | 11 ( 0.0) | ||||
| PUREN | 169 ( 0.1) | 262 ( 0.1) | ||||
| PURRANQUE | 238 ( 0.1) | 366 ( 0.1) | ||||
| PUTAENDO | 57 ( 0.0) | 129 ( 0.0) | ||||
| PUTRE | 14 ( 0.0) | 8 ( 0.0) | ||||
| PUYEHUE | 17 ( 0.0) | 27 ( 0.0) | ||||
| QUEILEN | 7 ( 0.0) | 10 ( 0.0) | ||||
| QUELLON | 549 ( 0.3) | 683 ( 0.2) | ||||
| QUEMCHI | 14 ( 0.0) | 56 ( 0.0) | ||||
| QUILACO | 14 ( 0.0) | 39 ( 0.0) | ||||
| QUILICURA | 2387 ( 1.2) | 4480 ( 1.2) | ||||
| QUILLECO | 12 ( 0.0) | 20 ( 0.0) | ||||
| QUILLON | 67 ( 0.0) | 165 ( 0.0) | ||||
| QUILLOTA | 1013 ( 0.5) | 3101 ( 0.8) | ||||
| QUILPUE | 887 ( 0.5) | 1747 ( 0.5) | ||||
| QUINCHAO | 14 ( 0.0) | 35 ( 0.0) | ||||
| QUINTA DE TILCOCO | 76 ( 0.0) | 112 ( 0.0) | ||||
| QUINTA NORMAL | 1434 ( 0.8) | 2362 ( 0.6) | ||||
| QUINTERO | 735 ( 0.4) | 1285 ( 0.3) | ||||
| QUIRIHUE | 35 ( 0.0) | 97 ( 0.0) | ||||
| RANCAGUA | 4382 ( 2.3) | 7505 ( 2.0) | ||||
| RANQUIL | 13 ( 0.0) | 20 ( 0.0) | ||||
| RAUCO | 27 ( 0.0) | 65 ( 0.0) | ||||
| RECOLETA | 2383 ( 1.2) | 3869 ( 1.0) | ||||
| RENAICO | 177 ( 0.1) | 219 ( 0.1) | ||||
| RENCA | 1676 ( 0.9) | 3373 ( 0.9) | ||||
| RENGO | 584 ( 0.3) | 1554 ( 0.4) | ||||
| REQUINOA | 115 ( 0.1) | 242 ( 0.1) | ||||
| RETIRO | 26 ( 0.0) | 41 ( 0.0) | ||||
| RINCONADA | 43 ( 0.0) | 123 ( 0.0) | ||||
| RIO BUENO | 233 ( 0.1) | 413 ( 0.1) | ||||
| RIO CLARO | 38 ( 0.0) | 50 ( 0.0) | ||||
| RIO HURTADO | 5 ( 0.0) | 10 ( 0.0) | ||||
| RIO IBAQEZ | 9 ( 0.0) | 14 ( 0.0) | ||||
| RIO NEGRO | 100 ( 0.1) | 154 ( 0.0) | ||||
| RIO VERDE | 2 ( 0.0) | 1 ( 0.0) | ||||
| ROMERAL | 110 ( 0.1) | 241 ( 0.1) | ||||
| SAAVEDRA | 31 ( 0.0) | 48 ( 0.0) | ||||
| SAGRADA FAMILIA | 155 ( 0.1) | 213 ( 0.1) | ||||
| SALAMANCA | 63 ( 0.0) | 122 ( 0.0) | ||||
| SAN ANTONIO | 1286 ( 0.7) | 2594 ( 0.7) | ||||
| SAN BERNARDO | 4673 ( 2.4) | 7234 ( 1.9) | ||||
| SAN CARLOS | 113 ( 0.1) | 267 ( 0.1) | ||||
| SAN CLEMENTE | 203 ( 0.1) | 546 ( 0.1) | ||||
| SAN ESTEBAN | 135 ( 0.1) | 160 ( 0.0) | ||||
| SAN FABIAN | 10 ( 0.0) | 10 ( 0.0) | ||||
| SAN FELIPE | 839 ( 0.4) | 1650 ( 0.4) | ||||
| SAN FERNANDO | 1596 ( 0.8) | 3365 ( 0.9) | ||||
| SAN IGNACIO | 39 ( 0.0) | 36 ( 0.0) | ||||
| SAN JAVIER | 306 ( 0.2) | 859 ( 0.2) | ||||
| SAN JOAQUIN | 1428 ( 0.7) | 2406 ( 0.6) | ||||
| SAN JOSE DE MAIPO | 298 ( 0.2) | 412 ( 0.1) | ||||
| SAN JUAN DE LA COSTA | 23 ( 0.0) | 37 ( 0.0) | ||||
| SAN MIGUEL | 1481 ( 0.8) | 3120 ( 0.8) | ||||
| SAN NICOLAS | 12 ( 0.0) | 9 ( 0.0) | ||||
| SAN PABLO | 29 ( 0.0) | 41 ( 0.0) | ||||
| SAN PEDRO | 63 ( 0.0) | 80 ( 0.0) | ||||
| SAN PEDRO DE ATACAMA | 148 ( 0.1) | 329 ( 0.1) | ||||
| SAN PEDRO DE LA PAZ | 867 ( 0.5) | 1382 ( 0.4) | ||||
| SAN RAFAEL | 34 ( 0.0) | 64 ( 0.0) | ||||
| SAN RAMON | 1635 ( 0.9) | 3243 ( 0.9) | ||||
| SAN ROSENDO | 12 ( 0.0) | 24 ( 0.0) | ||||
| SAN VICENTE | 679 ( 0.4) | 1347 ( 0.4) | ||||
| SANTA BARBARA | 223 ( 0.1) | 302 ( 0.1) | ||||
| SANTA CRUZ | 626 ( 0.3) | 1634 ( 0.4) | ||||
| SANTA JUANA | 121 ( 0.1) | 205 ( 0.1) | ||||
| SANTA MARIA | 73 ( 0.0) | 154 ( 0.0) | ||||
| SANTIAGO | 5807 ( 3.0) | 11268 ( 3.0) | ||||
| SANTO DOMINGO | 26 ( 0.0) | 92 ( 0.0) | ||||
| SIERRA GORDA | 13 ( 0.0) | 29 ( 0.0) | ||||
| TALAGANTE | 1440 ( 0.8) | 2941 ( 0.8) | ||||
| TALCA | 2813 ( 1.5) | 5418 ( 1.4) | ||||
| TALCAHUANO | 1332 ( 0.7) | 2677 ( 0.7) | ||||
| TALTAL | 312 ( 0.2) | 900 ( 0.2) | ||||
| TEMUCO | 1846 ( 1.0) | 2863 ( 0.8) | ||||
| TENO | 118 ( 0.1) | 225 ( 0.1) | ||||
| TEODORO SCHMIDT | 27 ( 0.0) | 46 ( 0.0) | ||||
| TIERRA AMARILLA | 200 ( 0.1) | 276 ( 0.1) | ||||
| TIL TIL | 281 ( 0.1) | 509 ( 0.1) | ||||
| TINAUKEL | 2 ( 0.0) | 1 ( 0.0) | ||||
| TIRUA | 26 ( 0.0) | 36 ( 0.0) | ||||
| TOCOPILLA | 624 ( 0.3) | 1436 ( 0.4) | ||||
| TOLTEN | 14 ( 0.0) | 25 ( 0.0) | ||||
| TOME | 478 ( 0.3) | 995 ( 0.3) | ||||
| TORRES DEL PAINE | 1 ( 0.0) | 1 ( 0.0) | ||||
| TORTEL | 10 ( 0.0) | 55 ( 0.0) | ||||
| TRAIGUEN | 68 ( 0.0) | 130 ( 0.0) | ||||
| TREGUACO | 9 ( 0.0) | 23 ( 0.0) | ||||
| TUCAPEL | 36 ( 0.0) | 50 ( 0.0) | ||||
| VALDIVIA | 2034 ( 1.1) | 3816 ( 1.0) | ||||
| VALLENAR | 1003 ( 0.5) | 2203 ( 0.6) | ||||
| VALPARAISO | 3548 ( 1.9) | 6854 ( 1.8) | ||||
| VICHUQUEN | 17 ( 0.0) | 21 ( 0.0) | ||||
| VICTORIA | 368 ( 0.2) | 837 ( 0.2) | ||||
| VICUÑA | 319 ( 0.2) | 797 ( 0.2) | ||||
| VILCUN | 40 ( 0.0) | 75 ( 0.0) | ||||
| VILLA ALEGRE | 157 ( 0.1) | 272 ( 0.1) | ||||
| VILLA ALEMANA | 671 ( 0.4) | 1585 ( 0.4) | ||||
| VILLARRICA | 395 ( 0.2) | 616 ( 0.2) | ||||
| VIQA DEL MAR | 3062 ( 1.6) | 6994 ( 1.9) | ||||
| VITACURA | 281 ( 0.1) | 548 ( 0.1) | ||||
| YERBAS BUENAS | 30 ( 0.0) | 80 ( 0.0) | ||||
| YUMBEL | 70 ( 0.0) | 210 ( 0.1) | ||||
| YUNGAY | 42 ( 0.0) | 92 ( 0.0) | ||||
| ZAPALLAR | 22 ( 0.0) | 57 ( 0.0) | ||||
| 201 ( 0.1) | 847 ( 0.2) | |||||
| region_delito (%) | RM Centro Norte | 11 ( 0.0) | 7 ( 0.0) | <0.001 | 0.023 | |
| RM Occidente | 3 ( 0.0) | 10 ( 0.0) | ||||
| RM Oriente | 2 ( 0.0) | 7 ( 0.0) | ||||
| RM Sur | 1 ( 0.0) | 98 ( 0.0) | ||||
| 191148 (100.0) | 377010 (100.0) | |||||
| medidas_155 (%) | SI | 11043 ( 5.8) | 46334 ( 12.3) | <0.001 | 0.229 | |
| 180122 ( 94.2) | 330798 ( 87.7) | |||||
| medidas_pp (%) | SI | 4559 ( 2.4) | 17737 ( 4.7) | <0.001 | 0.126 | |
| 186606 ( 97.6) | 359395 ( 95.3) | |||||
| medidas_ip (%) | SI | 325 ( 0.2) | 408 ( 0.1) | <0.001 | 0.017 | |
| 190840 ( 99.8) | 376724 ( 99.9) | |||||
| marca_suspension_43 (%) | NO | 7257 ( 3.8) | 59938 ( 15.9) | <0.001 | 0.424 | |
| SI | 210 ( 0.1) | 2131 ( 0.6) | ||||
| 183698 ( 96.1) | 315063 ( 83.5) | |||||
| marca_pena_44 (%) | SI | 7467 ( 3.9) | 62069 ( 16.5) | <0.001 | 0.424 | |
| 183698 ( 96.1) | 315063 ( 83.5) | |||||
| marca_multa_45 (%) | NO | 5729 ( 3.0) | 29491 ( 7.8) | <0.001 | 0.441 | |
| SI | 1738 ( 0.9) | 32578 ( 8.6) | ||||
| 183698 ( 96.1) | 315063 ( 83.5) | |||||
| medida_alternativa_46 (%) | Libertad Vigilada | 390 ( 0.2) | 2829 ( 0.8) | <0.001 | 0.329 | |
| Otra | 389 ( 0.2) | 4599 ( 1.2) | ||||
| Reclusión Nocturna | 1255 ( 0.7) | 12803 ( 3.4) | ||||
| Remisión Condicional | 1932 ( 1.0) | 16413 ( 4.4) | ||||
| 187199 ( 97.9) | 340488 ( 90.3) | |||||
| clasificacion_pena_47 (%) | Presidio Mayor grado máximo | 40 ( 0.0) | 48 ( 0.0) | <0.001 | 0.434 | |
| Presidio Mayor grado medio | 161 ( 0.1) | 307 ( 0.1) | ||||
| Presidio Mayor grado mínimo | 363 ( 0.2) | 1564 ( 0.4) | ||||
| Presidio Menor grado máximo | 723 ( 0.4) | 4267 ( 1.1) | ||||
| Presidio Menor grado medio | 1103 ( 0.6) | 7695 ( 2.0) | ||||
| Presidio Menor grado mínimo | 3737 ( 2.0) | 25823 ( 6.8) | ||||
| Presidio Perpetuo calificado | 0 ( 0.0) | 1 ( 0.0) | ||||
| Presidio Perpetuo simple | 3 ( 0.0) | 12 ( 0.0) | ||||
| Prisión | 1337 ( 0.7) | 22352 ( 5.9) | ||||
| 183698 ( 96.1) | 315063 ( 83.5) | |||||
| marca_suspension_51 (%) | NO | 10 ( 0.0) | 64 ( 0.0) | 0.001 | 0.011 | |
| SI | 1 ( 0.0) | 2 ( 0.0) | ||||
| 191154 (100.0) | 377066 (100.0) | |||||
| marca_pena_52 (%) | SI | 11 ( 0.0) | 66 ( 0.0) | 0.001 | 0.011 | |
| 191154 (100.0) | 377066 (100.0) | |||||
| marca_multa_53 (%) | NO | 8 ( 0.0) | 38 ( 0.0) | 0.001 | 0.011 | |
| SI | 3 ( 0.0) | 28 ( 0.0) | ||||
| 191154 (100.0) | 377066 (100.0) | |||||
| medida_alternativa_54 (%) | Libertad Vigilada | 0 ( 0.0) | 8 ( 0.0) | 0.003 | 0.013 | |
| Otra | 0 ( 0.0) | 5 ( 0.0) | ||||
| Reclusión Nocturna | 3 ( 0.0) | 14 ( 0.0) | ||||
| Remisión Condicional | 0 ( 0.0) | 15 ( 0.0) | ||||
| 191162 (100.0) | 377090 (100.0) | |||||
| clasificacion_pena_55 (%) | Presidio Mayor grado medio | 3 ( 0.0) | 0 ( 0.0) | <0.001 | 0.015 | |
| Presidio Mayor grado mínimo | 0 ( 0.0) | 5 ( 0.0) | ||||
| Presidio Menor grado máximo | 2 ( 0.0) | 10 ( 0.0) | ||||
| Presidio Menor grado medio | 3 ( 0.0) | 6 ( 0.0) | ||||
| Presidio Menor grado mínimo | 2 ( 0.0) | 19 ( 0.0) | ||||
| Prisión | 1 ( 0.0) | 26 ( 0.0) | ||||
| 191154 (100.0) | 377066 (100.0) | |||||
| Note. Continuous variables are presented as Medians and Percentiles 25 and 75 were shown; | ||||||
| Categorical variables are presented as number (%) |
rbind(
cbind(cat="Date of comission of the crime",
base_fiscalia %>%
dplyr::summarise(min = min(fec_comision_simple, na.rm=T),
p025=as.Date(quantile(unclass(fec_comision_simple), .025, na.rm=T), origin = "1970-01-01"),
p25=as.Date(quantile(unclass(fec_comision_simple), .25, na.rm=T), origin = "1970-01-01"),
p50=as.Date(quantile(unclass(fec_comision_simple), .5, na.rm=T), origin = "1970-01-01"),
p75=as.Date(quantile(unclass(fec_comision_simple), .75, na.rm=T), origin = "1970-01-01"),
p975=as.Date(quantile(unclass(fec_comision_simple), .975, na.rm=T), origin = "1970-01-01"),
max = max(fec_comision_simple, na.rm=T))),
cbind(cat="Date of termination of the relationship",
base_fiscalia %>%
dplyr::summarise(min = min(termino_relacion_simple, na.rm=T),
p025=as.Date(quantile(unclass(termino_relacion_simple), .025, na.rm=T), origin = "1970-01-01"),
p25=as.Date(quantile(unclass(termino_relacion_simple), .25, na.rm=T), origin = "1970-01-01"),
p50=as.Date(quantile(unclass(termino_relacion_simple), .5, na.rm=T), origin = "1970-01-01"),
p75=as.Date(quantile(unclass(termino_relacion_simple), .75, na.rm=T), origin = "1970-01-01"),
p975=as.Date(quantile(unclass(termino_relacion_simple), .975, na.rm=T), origin = "1970-01-01"),
max = max(termino_relacion_simple, na.rm=T))),
cbind(cat="Date of cbiorelacion",
base_fiscalia %>%
dplyr::summarise(min = min(fec_cbiorelacion_simple, na.rm=T),
p025=as.Date(quantile(unclass(fec_cbiorelacion_simple), .025, na.rm=T), origin = "1970-01-01"),
p25=as.Date(quantile(unclass(fec_cbiorelacion_simple), .25, na.rm=T), origin = "1970-01-01"),
p50=as.Date(quantile(unclass(fec_cbiorelacion_simple), .5, na.rm=T), origin = "1970-01-01"),
p75=as.Date(quantile(unclass(fec_cbiorelacion_simple), .75, na.rm=T), origin = "1970-01-01"),
p975=as.Date(quantile(unclass(fec_cbiorelacion_simple), .975, na.rm=T), origin = "1970-01-01"),
max = max(fec_cbiorelacion_simple, na.rm=T)))
) %>%
knitr::kable(format="html",caption= "Table 3. Summary of Dates") %>% #,col.names=c("Variables","Residential", "Ambulatory", "p-value")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover","condensed"),font_size= 10)| cat | min | p025 | p25 | p50 | p75 | p975 | max |
|---|---|---|---|---|---|---|---|
| Date of comission of the crime | 1900-01-01 | 2009-04-01 | 2011-12-09 | 2014-04-22 | 2016-11-14 | 2019-06-10 | 2019-12-31 |
| Date of termination of the relationship | 2002-10-24 | 2010-04-09 | 2012-06-06 | 2014-11-13 | 2017-06-06 | 2019-09-26 | 2021-07-20 |
| Date of cbiorelacion | 2002-10-24 | 2010-04-09 | 2012-06-06 | 2014-11-13 | 2017-06-06 | 2019-09-26 | 2021-07-20 |
Missingness
cols<-base_fiscalia[,myVars] %>%
names() #si fuera desde SPSS, sería p47_demo_medio_transp_otr
tot<-
base_fiscalia %>%
# dplyr::filter(resp_status=="partial") %>%
nrow()
#Ordenar variables
missing.values <-
base_fiscalia %>%
tidyr::gather() %>%
dplyr::mutate(is.missing = is.na(value)) %>%
dplyr::group_by(key, is.missing) %>%
dplyr::summarise(num.missing = n()) %>%
dplyr::filter(is.missing==T) %>%
dplyr::mutate(perc_miss=num.missing/tot) %>%
dplyr::mutate(label_text=paste0("%= ", scales::percent(num.missing/tot, accuracy=1L),"\n N= ", format(num.missing,big.mark=","))) %>%
dplyr::select(-is.missing) %>%
dplyr::ungroup() %>%
dplyr::group_by(key) %>%
dplyr::slice(1) %>%
dplyr::ungroup()## Warning: attributes are not identical across measure variables;
## they will be dropped## `summarise()` has grouped output by 'key'. You can override using the `.groups` argument.plot_miss<-
missing.values %>%
dplyr::ungroup() %>%
# slice(1:29) %>%
ggplot() +
geom_bar(aes(x=forcats::fct_reorder(key, perc_miss), y=perc_miss, label=label_text), stat = 'identity') +
labs(x='variable', y="Porcentaje de perdidos", caption=paste0("Nota. Porcentaje de perdidos del total (",format(tot, big.mark=","),")")) +
#theme(axis.text.x = element_text(angle = 45, hjust = 1, size=6))+
theme(axis.text.x = element_blank())+
scale_y_continuous(limits=c(0,1))+
sjPlot::theme_sjplot()+
coord_flip()## Warning: Ignoring unknown aesthetics: label ggplotly(plot_miss, tooltip = c("label_text"))%>% layout(xaxis= list(showticklabels = T), height = 600, width=800) %>% layout(xaxis = list(tickformat='%', range = c(0, 1)))## Warning: Specifying width/height in layout() is now deprecated.
## Please specify in ggplotly() or plot_ly()Near Zero Variance
Check variables that only one unique value (i.e. are zero variance) or predictors that have both of the following characteristics: few unique values given the sample size, and the frequency of the most common value is much more big than the second most common value.
nzv_vals <- nearZeroVar(base_fiscalia, saveMetrics = TRUE)
nzv_sorted <- arrange(nzv_vals, desc(freqRatio))
kable(as.data.frame.TableOne(nzv_sorted), size=11, format="html",
caption= "Table 4. Near Zero Variance",
col.names=c("Variable", "freqRatio", "% of Unique values", "Zero Variance", "Near Zero Variance")) %>% #,col.names=c("Variables","Residential", "Ambulatory", "p-value")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover","condensed"),font_size= 10) %>%
kableExtra::add_footnote(c("Note. freqRatio= ratio of frequencies for the most common value over the second most common value for that variable"), notation = "none")%>%
kableExtra::scroll_box(width = "100%", height = "375px")| Variable | freqRatio | % of Unique values | Zero Variance | Near Zero Variance |
|---|---|---|---|---|
| tipo_sujeto_vic | 1664.733333 | 0.0014077 | FALSE | TRUE |
| gls_tipo_sujeto_vic | 1664.733333 | 0.0014077 | FALSE | TRUE |
| idsujeto_victima | 277.615385 | 89.6786363 | FALSE | FALSE |
| marca_suspension_43 | 28.703546 | 0.0003519 | FALSE | TRUE |
| marca_suspension_51 | 24.666667 | 0.0003519 | FALSE | TRUE |
| impcod_tiposujeto | 18.980421 | 0.0010558 | FALSE | FALSE |
| gls_tipo_imputado | 18.980421 | 0.0010558 | FALSE | FALSE |
| cod_sitiosuceso | 10.792359 | 0.0026395 | FALSE | FALSE |
| cod_motsuspension | 8.865668 | 0.0010558 | FALSE | FALSE |
| region_delito | 5.500000 | 0.0007039 | FALSE | FALSE |
| cod_region | 4.518480 | 0.0036953 | FALSE | FALSE |
| relacion_vifsaf | 3.906006 | 0.0003519 | FALSE | FALSE |
| gls_motsuspension | 3.599314 | 0.0008798 | FALSE | FALSE |
| fec_comision | 2.809524 | 60.2693662 | FALSE | FALSE |
| gls_proctermino | 2.624413 | 0.0012318 | FALSE | FALSE |
| cod_proctermino | 2.524388 | 0.0014077 | FALSE | FALSE |
| idsujeto_imputado | 2.303571 | 90.5061966 | FALSE | FALSE |
| encontrado_como_imputado | 1.972809 | 0.0003519 | FALSE | FALSE |
| encontrado_como_victima | 1.967128 | 0.0003519 | FALSE | FALSE |
| rut_enc_saf | 1.830986 | 13.1679386 | FALSE | FALSE |
| iddelito | 1.518519 | 92.9843022 | FALSE | FALSE |
| cod_mottermino | 1.494797 | 0.0045751 | FALSE | FALSE |
| gls_mottermino | 1.494797 | 0.0043991 | FALSE | FALSE |
| cod_delito | 1.487699 | 0.0622914 | FALSE | FALSE |
| gls_materia | 1.487699 | 0.0622914 | FALSE | FALSE |
| marca_multa_53 | 1.483871 | 0.0003519 | FALSE | FALSE |
| cod_parentescoimputado | 1.424809 | 0.0043991 | FALSE | FALSE |
| gls_parentesco | 1.424809 | 0.0043991 | FALSE | FALSE |
| tipo_termino | 1.326149 | 0.0005279 | FALSE | FALSE |
| medida_alternativa_46 | 1.304951 | 0.0007039 | FALSE | FALSE |
| clasificacion_pena_55 | 1.285714 | 0.0010558 | FALSE | FALSE |
| termino_relacion | 1.250549 | 0.7647410 | FALSE | FALSE |
| fec_cbiorelacion_simple | 1.250549 | 0.7647410 | FALSE | FALSE |
| termino_relacion_simple | 1.250549 | 0.7647410 | FALSE | FALSE |
| clasificacion_pena_47 | 1.247837 | 0.0015837 | FALSE | FALSE |
| fec_cbiorelacion | 1.236967 | 7.7364477 | FALSE | FALSE |
| reg | 1.229971 | 0.0033433 | FALSE | FALSE |
| gls_region | 1.229971 | 0.0007039 | FALSE | FALSE |
| ruc | 1.216981 | 87.0898491 | FALSE | FALSE |
| familia_delito | 1.200839 | 0.0036953 | FALSE | FALSE |
| medida_alternativa_54 | 1.133333 | 0.0007039 | FALSE | FALSE |
| gls_sitiosuceso | 1.054073 | 0.0095021 | FALSE | FALSE |
| agrupa_terminos | 1.050209 | 0.0028154 | FALSE | FALSE |
| cod_lugarocurrencia | 1.037330 | 0.0017596 | FALSE | FALSE |
| lugar_ocurrencia | 1.037330 | 0.0015837 | FALSE | FALSE |
| marca_multa_45 | 1.026343 | 0.0003519 | FALSE | FALSE |
| cod_comunadelito | 1.009519 | 0.0656347 | FALSE | FALSE |
| gls_comuna | 1.008743 | 0.0608837 | FALSE | FALSE |
| fec_comision_simple | 1.002660 | 0.9808252 | FALSE | FALSE |
| idrelacion | 1.000000 | 99.9987682 | FALSE | FALSE |
| medidas_155 | 0.000000 | 0.0001760 | TRUE | TRUE |
| medidas_pp | 0.000000 | 0.0001760 | TRUE | TRUE |
| medidas_ip | 0.000000 | 0.0001760 | TRUE | TRUE |
| marca_pena_44 | 0.000000 | 0.0001760 | TRUE | TRUE |
| tramos_condena_48 | 0.000000 | 0.0000000 | TRUE | TRUE |
| clasificacion_penarpa_1_49 | 0.000000 | 0.0000000 | TRUE | TRUE |
| clasificacion_penarpa_2_50 | 0.000000 | 0.0000000 | TRUE | TRUE |
| marca_pena_52 | 0.000000 | 0.0001760 | TRUE | TRUE |
| tramos_condena_56 | 0.000000 | 0.0000000 | TRUE | TRUE |
| clasificacion_penarpa_1_57 | 0.000000 | 0.0000000 | TRUE | TRUE |
| clasificacion_penarpa_2_58 | 0.000000 | 0.0000000 | TRUE | TRUE |
| Note. freqRatio= ratio of frequencies for the most common value over the second most common value for that variable |
Times as accused
- 366 people that is found as accused and as a victim at the same time. Meanwhile, we considered them as accused.
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
base_fiscalia_acc_imp<-
base_fiscalia %>%
dplyr::mutate(accused=dplyr::case_when(encontrado_como_victima=="SI" & encontrado_como_imputado=="SI"~"IMPUTADO",
encontrado_como_victima=="NO" & encontrado_como_imputado=="SI"~"IMPUTADO",
T~"VICTIMA")) %>%
dplyr::filter(accused=="IMPUTADO") %>%
dplyr::group_by(rut_enc_saf) %>%
dplyr::count() %>%
ungroup()
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
p<-
base_fiscalia_acc_imp %>%
dplyr::left_join(CONS_C1_df_dup_SEP_2020[,c("hash_key","dup")] %>% distinct(hash_key, .keep_all = T),by=c("rut_enc_saf"="hash_key")) %>% ggplot(aes(x=n), alpha=.7)+
geom_histogram_interactive(bins=120)+
geom_vline(aes(xintercept=median(base_fiscalia_acc_imp$n)), color="darkred", linetype="dashed")+
geom_vline(aes(xintercept=quantile(base_fiscalia_acc_imp$n, .99)), color="darkmagenta", linetype="solid")+
geom_vline(aes(xintercept=quantile(base_fiscalia_acc_imp$n, .95)), color="darkblue", linetype="dotted")+
sjPlot::theme_sjplot2() +
#facet_wrap(~motivodeegreso_mod_imp)+
labs(y = "Frequency",x="Diff. Between Treatments", caption= paste0("Blue dotted line=", quantile(base_fiscalia_acc_imp$n, .95)," records; Violet solid line=",quantile(base_fiscalia_acc_imp$n, .99)," records; Red dashed line= Median=",median(base_fiscalia_acc_imp$n)," records; Maximum=",max(base_fiscalia_acc_imp$n)," records"))
p
Figure 1. Distribution of records in which a user is declared as an accused
Times as victim
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
base_fiscalia_acc_vic<-
base_fiscalia %>%
dplyr::mutate(accused=dplyr::case_when(encontrado_como_victima=="SI" & encontrado_como_imputado=="SI"~"IMPUTADO",
encontrado_como_victima=="NO" & encontrado_como_imputado=="SI"~"IMPUTADO",
T~"VICTIMA")) %>%
dplyr::filter(accused=="VICTIMA") %>%
dplyr::group_by(rut_enc_saf) %>%
dplyr::count() %>%
ungroup()
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
p2<-
base_fiscalia_acc_vic %>%
dplyr::left_join(CONS_C1_df_dup_SEP_2020[,c("hash_key","dup")] %>% distinct(hash_key, .keep_all = T),by=c("rut_enc_saf"="hash_key")) %>% ggplot(aes(x=n), alpha=.7)+
geom_histogram_interactive(bins=120)+
geom_vline(aes(xintercept=median(base_fiscalia_acc_vic$n)), color="darkred", linetype="dashed")+
geom_vline(aes(xintercept=quantile(base_fiscalia_acc_vic$n, .99)), color="darkmagenta", linetype="solid")+
geom_vline(aes(xintercept=quantile(base_fiscalia_acc_vic$n, .95)), color="darkblue", linetype="dotted")+
sjPlot::theme_sjplot2() +
#facet_wrap(~motivodeegreso_mod_imp)+
labs(y = "Frequency",x="Diff. Between Treatments", caption= paste0("Blue dotted line=", quantile(base_fiscalia_acc_vic$n, .95)," records; Violet solid line=",quantile(base_fiscalia_acc_vic$n, .99)," records; Red dashed line= Median=",median(base_fiscalia_acc_vic$n)," records; Maximum=",max(base_fiscalia_acc_vic$n)," records"))
# xlim(c(-1,2000))
p2
Figure 2. Distribution of records in which a user is declared as a Victim
paste0("Percentage of HASHs that have records as victims: ",
scales::percent(
base_fiscalia_acc_vic %>%
dplyr::left_join(CONS_C1_df_dup_SEP_2020[,c("hash_key","dup")] %>% distinct(hash_key, .keep_all = T),by=c("rut_enc_saf"="hash_key")) %>% distinct(rut_enc_saf) %>% nrow()/CONS_C1_df_dup_SEP_2020[,c("hash_key","dup")] %>% distinct(hash_key, .keep_all = T) %>% nrow(),
accuracy=.01
)
)## [1] "Percentage of HASHs that have records as victims: 67.40%"paste0("Percentage of HASHs that have records as accused: ",
scales::percent(
base_fiscalia_acc_vic %>%
dplyr::left_join(CONS_C1_df_dup_SEP_2020[,c("hash_key","dup")] %>% distinct(hash_key, .keep_all = T),by=c("rut_enc_saf"="hash_key")) %>% distinct(rut_enc_saf) %>% nrow()/CONS_C1_df_dup_SEP_2020[,c("hash_key","dup")] %>% distinct(hash_key, .keep_all = T) %>% nrow(),
accuracy=.01
)
)## [1] "Percentage of HASHs that have records as accused: 67.40%"Structure
The hypothetized structure would be HASHs (rut_enc_saf), RUCs (ruc), the combination of an specific accused (idsujeto_imputado) and victim (idsujeto_victima), ending with a relationship (idrelacion) as the result of this combination with an specific type of crime/offence (id_delito) in a determined court case.
Some counts of different values by each variable.
id_suj_vic<-
cbind(cat="Number of entries by ID of victim",
base_fiscalia %>%
group_by(idsujeto_victima) %>%
summarise(n=n()) %>%
summarise(min=min(n), max=max(n), mean=mean(n), median=median(n), p025=quantile(n, .025), p25=quantile(n, .25), p75=quantile(n,.75), p975=quantile(n, .975)))
#podría agrupar al resto. De hecho, hay un ID que agrupa a 18,045 filas, que es el id sujeto ==0
if(no_mostrar==0){
base_fiscalia %>%
dplyr::filter(ruc %in% as.character(unlist(base_fiscalia %>%
dplyr::filter(idsujeto_victima==0) %>%
dplyr::select(ruc)))) %>%
dplyr::group_by(idrelacion) %>%
summarise(n=n()) %>%
summarise(min=min(n), max=max(n), mean=mean(n), median=median(n), p025=quantile(n, .025), p25=quantile(n, .25), p75=quantile(n,.75), p975=quantile(n, .975))
}
id_suj_imp<-
cbind(cat="Number of entries by ID of accused",
base_fiscalia %>%
group_by(idsujeto_imputado) %>%
summarise(n=n()) %>%
summarise(min=min(n), max=max(n), mean=mean(n), median=median(n), p025=quantile(n, .025), p25=quantile(n, .25), p75=quantile(n,.75), p975=quantile(n, .975)))
#agrupa al resto, me da la impresión, tiene hasta 129 filas con el mismo id sujeto imputado
ruc<-
cbind(cat="Number of entries by RUC",
base_fiscalia %>%
group_by(ruc) %>%
summarise(n=n()) %>%
summarise(min=min(n), max=max(n), mean=mean(n), median=median(n), p025=quantile(n, .025), p25=quantile(n, .25), p75=quantile(n,.75), p975=quantile(n, .975)))
# 1 0810024146-4 129
# 2 0901135475-8 106
# 3 1000499758-7 56
# 4 1000241245-K 49
#El ruc no es específico, tiene hasta 129 filas. Ver si tienen relación con los ID's sjeto imputado o víctima
id_delito<-
cbind(cat="Number of entries by ID delito",
base_fiscalia %>%
group_by(iddelito) %>%
summarise(n=n()) %>%
summarise(min=min(n), max=max(n), mean=mean(n), median=median(n), p025=quantile(n, .025), p25=quantile(n, .25), p75=quantile(n,.75), p975=quantile(n, .975)))
# algo específica, tiene como máximo 82 filas con el mismo id delito
id_rel<-
cbind(cat="Number of entries by ID relación",
base_fiscalia %>%
group_by(idrelacion) %>%
summarise(n=n()) %>%
summarise(min=min(n), max=max(n), mean=mean(n, na.rm=T), median=median(n, na.rm=T), p025=quantile(n, .025), p25=quantile(n, .25), p75=quantile(n,.75), p975=quantile(n, .975)))
# más específica, tiene como máximo 2 filas con el mismo id relación
no_mostrar=1
if(no_mostrar==0){
invisible("Para ver casos posiblemente duplicados")
base_fiscalia %>%
dplyr::mutate(combinacion=paste0(ruc,"_",rut_enc_saf)) %>%
dplyr::filter(combinacion %in% as.character(unlist(agrupacion_base %>%
dplyr::filter(max>1) %>%dplyr::mutate(combinacion=paste0(ruc,"_",rut_enc_saf)) %>% dplyr::select(combinacion)))) %>% View()
}
ruts<-cbind(cat="Number of entries by Ruts",
base_fiscalia %>%
group_by(rut_enc_saf) %>%
summarise(n=n()) %>%
summarise(min=min(n), max=max(n), mean=mean(n, na.rm=T), median=median(n, na.rm=T), p025=quantile(n, .025), p25=quantile(n, .25), p75=quantile(n,.75), p975=quantile(n, .975)))
ruc_por_rut<-
cbind(cat="Number of different Causes(RUCs) by RUT",
base_fiscalia %>%
group_by(rut_enc_saf) %>%
summarise(n=n_distinct(ruc)) %>%
summarise(min=min(n), max=max(n), mean=mean(n, na.rm=T), median=median(n, na.rm=T), p025=quantile(n, .025), p25=quantile(n, .25), p75=quantile(n,.75), p975=quantile(n, .975))) #%>% copiar_nombres()
del_por_ruc<-
cbind(cat="Number of different Crimes by RUT",
base_fiscalia %>%
group_by(rut_enc_saf) %>%
summarise(n=n_distinct(gls_materia)) %>%
summarise(min=min(n), max=max(n), mean=mean(n, na.rm=T), median=median(n, na.rm=T), p025=quantile(n, .025), p25=quantile(n, .25), p75=quantile(n,.75), p975=quantile(n, .975)))
rel_por_ruc<-
cbind(cat="Number of different relatonships by RUT",
base_fiscalia %>%
group_by(rut_enc_saf) %>%
summarise(n=n_distinct(idrelacion)) %>%
summarise(min=min(n), max=max(n), mean=mean(n, na.rm=T), median=median(n, na.rm=T), p025=quantile(n, .025), p25=quantile(n, .25), p75=quantile(n,.75), p975=quantile(n, .975)))# %>% copiar_nombres()
rel_por_ruc<-
cbind(cat="Number of different relatonships by Causes(RUC)",
base_fiscalia %>%
group_by(ruc) %>%
summarise(n=n_distinct(idrelacion)) %>%
summarise(min=min(n), max=max(n), mean=mean(n, na.rm=T), median=median(n, na.rm=T), p025=quantile(n, .025), p25=quantile(n, .25), p75=quantile(n,.75), p975=quantile(n, .975)))
delitos_ruc<-
cbind(cat="Number of different crimes by Causes(RUC)",base_fiscalia %>%
group_by(ruc) %>%
summarise(n=n_distinct(gls_materia)) %>%
summarise(min=min(n), max=max(n), mean=mean(n, na.rm=T), median=median(n, na.rm=T), p025=quantile(n, .025), p25=quantile(n, .25), p75=quantile(n,.75), p975=quantile(n, .975)))
invisible("Exploremos algunos")
# base_fiscalia %>%
# dplyr::filter(ruc %in% c("0810024146-4","0901135475-8")) %>% View()
invisible("ID sujeto imputado, RUC")
# base_fiscalia %>%
# group_by(ruc,idsujeto_imputado) %>%
# summarise(n=n()) %>%
# summarise(min=min(n), max=max(n), mean=mean(n), median=median(n), p025=quantile(n, .025), p25=quantile(n, .25), p75=quantile(n,.75), p975=quantile(n, .975))
# No correr, entrega 494,929 casos
invisible("ID sujeto víctima, RUC")
# base_fiscalia %>%
# group_by(idsujeto_victima,ruc) %>%
# summarise(n=n()) %>%
# summarise(min=min(n), max=max(n), mean=mean(n), median=median(n), p025=quantile(n, .025), p25=quantile(n, .25), p75=quantile(n,.75), p975=quantile(n, .975))
#no correr. son 514,344 casos
#si pongo al revés, con el RUC primero,
# Error: Problem with `summarise()` column `p75`.
# i `p75 = quantile(n, 0.75)`.
# x argument is not interpretable as logical
# i The error occurred in group 40782: ruc = "1000495180-3".
# Backtrace:
# 1. `%>%`(...)
# 9. base::.handleSimpleError(...)
# 10. dplyr:::h(simpleError(msg, call))
# In addition: Warning message:
# In if (na.rm) x <- x[!is.na(x)] else if (anyNA(x)) stop("missing values and NaN's not allowed if 'na.rm' is FALSE") :
# the condition has length > 1 and only the first element will be used
bind_rows(ruts,
id_suj_vic,
id_suj_imp,
ruc,
id_delito,
id_rel,
ruc_por_rut,
del_por_ruc,
rel_por_ruc,
delitos_ruc) %>%
dplyr::select(cat, everything()) %>%
knitr::kable(size=11, first.strip=T, hide.no="no",
format="html",caption= "Table 5. Counts of entries by different groups",
smd=T, test=T, varLabels=T,noSpaces=T, printToggle=T, dropEqual=F) %>% #,col.names=c("Variables","Residential", "Ambulatory", "p-value")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover","condensed"),font_size= 12) %>%
# kableExtra::add_footnote(c("Note. Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;", "Categorical variables are presented as number (%)"), notation = "none")%>%
kableExtra::scroll_box(width = "100%", height = "375px")| cat | min | max | mean | median | p025 | p25 | p75 | p975 |
|---|---|---|---|---|---|---|---|---|
| Number of entries by Ruts | 1 | 260 | 7.594203 | 5 | 1 | 2 | 10 | 29 |
| Number of entries by ID of victim | 1 | 18045 | 1.115093 | 1 | 1 | 1 | 1 | 2 |
| Number of entries by ID of accused | 1 | 129 | 1.104897 | 1 | 1 | 1 | 1 | 2 |
| Number of entries by RUC | 1 | 129 | 1.148239 | 1 | 1 | 1 | 1 | 2 |
| Number of entries by ID delito | 1 | 82 | 1.075450 | 1 | 1 | 1 | 1 | 2 |
| Number of entries by ID relación | 1 | 2 | 1.000012 | 1 | 1 | 1 | 1 | 1 |
| Number of different Causes(RUCs) by RUT | 1 | 246 | 6.710008 | 5 | 1 | 2 | 9 | 25 |
| Number of different Crimes by RUT | 1 | 26 | 4.594323 | 4 | 1 | 2 | 6 | 13 |
| Number of different relatonships by Causes(RUC) | 1 | 129 | 1.148225 | 1 | 1 | 1 | 1 | 2 |
| Number of different crimes by Causes(RUC) | 1 | 7 | 1.055796 | 1 | 1 | 1 | 1 | 2 |
We looked for the percentage of entries with the ID for the victim equal to 0 for each trimester, and the number of entries ithout For each Cuando hablamos de víctimas sin ID sujeto, para cada trimestre desde el año 1999, porcentaje del total de entradas y el número de entradas sin ID sujeto por el total. En el eje x, la fecha en que se comete el crimen
#sería RUT/Hash → RUC → relaciones
#
#fec_cbiorelacion
#fec_comision
#termino_relacion
invisible("¿Hay algún patrón por año?, ver el recuento de id_victimas==0 por cada trimestre y sacar un gráfico de densidad de eso")
require(zoo)
require(ggrepel)## Loading required package: ggrepelvic_trim_id_suj_0<-
base_fiscalia %>%
dplyr::arrange(rut_enc_saf, ruc, idrelacion) %>%
dplyr::mutate(fech_ing_qrt=zoo::as.yearqtr(fec_comision)) %>%
dplyr::mutate(idsujeto_victima_0=ifelse(idsujeto_victima==0,1,0)) %>%
dplyr::group_by(fech_ing_qrt) %>% #%>% janitor::tabyl(sus_ini)
dplyr::summarise(n=n(),
sum_0 = length(n[idsujeto_victima_0==1]),
perc = sum_0/n) %>%
dplyr::ungroup() %>%
dplyr::mutate(label_text= paste0("Year/Quarter= ",fech_ing_qrt,"\nTotal Entries= ",n,"\n %= ",scales::percent(perc, accuracy=1L),"\nEntries w/ NNs= ", sum_0)) %>%
dplyr::filter(fech_ing_qrt>=2000) %>%
dplyr::arrange(desc(fech_ing_qrt)) %>%
ggplot2::ggplot(aes(x = fech_ing_qrt, y = perc, label = label_text)) +
geom_line(color = "#0076A8", size=1) +
#geom_text_repel(aes(x = fech_ing_qrt, y = perc, label = paste0("Rows=",n,"\nNN victims= ", sum_0)), vjust = -1,hjust = 0, angle=45, #size=3, arrow = arrow(length = unit(0.01, 'npc'))) +
sjPlot::theme_sjplot2() +
labs(y="% of Missing Data of the Victim",x="Years & Quarters, Date of commission of the crime",caption="Note. Some crimes were commited before 1960 (n= 246), and in 1900 (n=241)") +
scale_y_continuous(labels = scales::percent) +
scale_x_yearqtr(format="%YQ%q", n=15) +
theme(axis.text.x = element_text(vjust = 0.5,hjust = 0.5,angle = 60), plot.caption=element_text(hjust=0))
ggplotly(vic_trim_id_suj_0, tooltip="label_text")vic_trim_id_suj_0_b<-
base_fiscalia %>%
dplyr::arrange(rut_enc_saf, ruc, idrelacion) %>%
dplyr::mutate(fech_ing_qrt=zoo::as.yearqtr(termino_relacion)) %>%
dplyr::mutate(idsujeto_victima_0=ifelse(idsujeto_victima==0,1,0)) %>%
dplyr::group_by(fech_ing_qrt) %>% #%>% janitor::tabyl(sus_ini)
dplyr::summarise(n=n(),
sum_0 = length(n[idsujeto_victima_0==1]),
perc = sum_0/n) %>%
dplyr::ungroup() %>%
dplyr::mutate(label_text= paste0("Year/Quarter= ",fech_ing_qrt,"\nTotal Entries= ",n,"\n %= ",scales::percent(perc, accuracy=1L),"\nEntries w/ NNs= ", sum_0)) %>%
dplyr::filter(fech_ing_qrt>=2000) %>%
dplyr::arrange(desc(fech_ing_qrt)) %>%
ggplot2::ggplot(aes(x = fech_ing_qrt, y = perc, label = label_text)) +
geom_line(color = "#0076A8", size=1) +
# geom_text_repel(aes(x = fech_ing_qrt, y = perc, label = paste0("Rows=",n,"\nNN victims= ", sum_0)), vjust = -1,hjust = 0, angle=45, size=3, arrow = arrow(length = unit(0.01, 'npc'))) +
sjPlot::theme_sjplot2() +
labs(y="% of Missing Data of the Victim",x="Years & Quarters, Date of termination of the relationship") +
scale_y_continuous(labels = scales::percent) +
scale_x_yearqtr(format="%YQ%q", n=30) +
theme(axis.text.x = element_text(vjust = 0.5,hjust = 0.5,angle = 60), plot.caption=element_text(hjust=0))
ggplotly(vic_trim_id_suj_0_b, tooltip="label_text")Some questions
# invisible("¿Puede que el RUC se repita para un mismo HASH?, ¿en qué situaciones?")
# base_fiscalia %>%
# dplyr::mutate(combinacion=paste0(ruc,"_",rut_enc_saf)) %>%
# dplyr::filter(combinacion %in% as.character(unlist(agrupacion_base %>%
# dplyr::filter(max>1) %>%dplyr::mutate(combinacion=paste0(ruc,"_",rut_enc_saf)) %>% dplyr::select(combinacion)))) %>% View()
cat("Entries with accused and victim status")## Entries with accused and victim statusformat(base_fiscalia %>%
dplyr::filter(encontrado_como_victima=="SI" & encontrado_como_imputado=="SI") %>% nrow(), big.mark=",")## [1] "366"cat("Differences between date of termination of cbiorelacion (fec_cbiorelacion) and termination of relationship (termino_relacion)")## Differences between date of termination of cbiorelacion (fec_cbiorelacion) and termination of relationship (termino_relacion)invisible("¿Hay diferencias en la fecha biorelacion y la de término de relación")
base_fiscalia %>%
dplyr::filter(termino_relacion!=fec_cbiorelacion) %>%
dplyr::mutate(diff_en_dias=as.numeric((termino_relacion-fec_cbiorelacion)/(60*24))) %>%
select(diff_en_dias) %>%
summarise(n=n(),min=min(diff_en_dias), max=max(diff_en_dias), mean=mean(diff_en_dias), median=median(diff_en_dias), p025=quantile(diff_en_dias, .025), p25=quantile(diff_en_dias, .25), p75=quantile(diff_en_dias,.75), p975=quantile(diff_en_dias, .975)) %>% copiar_nombres()## # A tibble: 1 x 9
## n min max mean median p025 p25 p75 p975
## <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 44930 -0.990 -0.0139 -0.558 -0.542 -0.797 -0.681 -0.434 -0.345cat("Entries in which the RUT is found as accused and victim (distinct RUTs & distinct RUCs)")## Entries in which the RUT is found as accused and victim (distinct RUTs & distinct RUCs)invisible("Casos en que hay filas en que un rut es imputado y víctima")
base_fiscalia %>%
dplyr::filter(encontrado_como_victima=="SI" & encontrado_como_imputado=="SI") %>%
summarise(n=n_distinct(rut_enc_saf), n2=n_distinct(ruc))## # A tibble: 1 x 2
## n n2
## <int> <int>
## 1 339 331cat("Entries in which an accused commits a crime with distinct cause (RUC) in the same date")## Entries in which an accused commits a crime with distinct cause (RUC) in the same dateinvisible("Casos en que una persona comete un delito con distinta causa en la misma fecha")
format(base_fiscalia %>%
dplyr::mutate(comb=paste0(rut_enc_saf,"_",fec_comision)) %>%
dplyr::add_count(comb, .keep=T) %>% filter(n > 1) %>%
dplyr::group_by(comb) %>%
dplyr::mutate(n=n_distinct(ruc)) %>%
dplyr::filter(n>1) %>%
nrow(), big.mark=",")## [1] "31,437" # dplyr::ungroup() %>%
# dplyr::arrange(rut_enc_saf, ruc) %>%
# dplyr::select(rut_enc_saf, ruc, idrelacion, gls_materia, fec_comision)
# Ejemplo de un caso
# 0007678b8b35fa0961d1e8110fbf9620 SI NO 6 VICTIMA 47350754 5 V Región de Valparaíso 16913947 1400066420-1 SALIDA NO JUDICIAL DECISI¿N DE NO PERSEVERAR 525 AMENAZAS CONDIC. CONTRA PERSONAS Y PROP. ART. 296 1 y 2, 297 DELITOS CONTRA LA LIBERTAD E INTIMIDAD DE LAS PERSONAS SI 16 CONVIVIENTE 5 Decisión de no perseverar en el proced 0 NA 1 ORDINARIO 47350753 3 IMPUTADO 12909542 18-01-2014 19:40 16-04-2014 2 LUGAR HABITADO O DESTINADO A LA HABITACION Y SUS DEPENDENCIAS 1 VIVIENDA 310 VALPARAISO 5 V Región de Valparaíso 16-04-2014 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
# 0007678b8b35fa0961d1e8110fbf9620 SI NO 6 VICTIMA 47359056 5 V Región de Valparaíso 16916932 1400068650-7 SALIDA NO JUDICIAL ARCHIVO PROVISIONAL 524 AMENAZAS SIMPLES CONTRA PERSONAS Y PROPIEDADES ART. 296 Nº3. DELITOS CONTRA LA LIBERTAD E INTIMIDAD DE LAS PERSONAS SI 16 CONVIVIENTE 1 Archivo Provisional 0 NA 0 SIN PROCEDIMIENTO 47359057 3 IMPUTADO 12911972 18-01-2014 19:40 27-01-2014 2 LUGAR HABITADO O DESTINADO A LA HABITACION Y SUS DEPENDENCIAS 1 VIVIENDA 310 VALPARAISO 5 V Región de Valparaíso 27-01-2014 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
cat("Entries that have different relationship ID for the same combination of an ID of the victim and the ID of the accused")## Entries that have different relationship ID for the same combination of an ID of the victim and the ID of the accusedinvisible("Gente que tendría distinto id de relación para una misma combianción de id víctima e id imputado")
format(base_fiscalia %>%
dplyr::mutate(comb=paste0(idsujeto_victima,"_",idsujeto_imputado)) %>%
dplyr::add_count(comb, .keep=T) %>% filter(n > 1) %>%
dplyr::group_by(comb) %>%
dplyr::summarise(n=n_distinct(idrelacion)) %>%
dplyr::filter(!grepl("^0_", comb)) %>%
dplyr::filter(n>1) %>% nrow(), big.mark=",")## [1] "21,393"cat("Example")## Examplebase_fiscalia %>%
dplyr::mutate(comb=paste0(idsujeto_victima,"_",idsujeto_imputado)) %>%
arrange(rut_enc_saf,fec_comision,ruc,idrelacion) %>%
dplyr::filter(comb=="10760610_10760611") %>%
dplyr::select(rut_enc_saf, ruc, idsujeto_victima, idsujeto_imputado, idrelacion, gls_materia) %>% print()## # A tibble: 2 x 6
## rut_enc_saf ruc idsujeto_victima idsujeto_imputa~ idrelacion gls_materia
## <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 daee9f4184b1~ 07000~ 10760610 10760611 4201225 VIOLACIÓN D~
## 2 daee9f4184b1~ 07000~ 10760610 10760611 4040665 LESIONES LE~cat("Cases with date of crime comission are posterior to the date that the relationship ended")## Cases with date of crime comission are posterior to the date that the relationship endedformat(base_fiscalia %>%
dplyr::mutate(fec_comision_simple=as.Date(stringr::str_extract(as.character(fec_comision), "^.{10}"))) %>%
dplyr::mutate(termino_relacion_simple=as.Date(stringr::str_extract(as.character(termino_relacion), "^.{10}"))) %>%
dplyr::mutate(diff_en_dias=as.numeric((termino_relacion_simple-fec_comision_simple))) %>%
dplyr::filter(diff_en_dias<0) %>%
nrow(), big.mark=",")## [1] "68" #dplyr::select(rut_enc_saf,ruc, fec_comision, termino_relacion, diff_en_dias) %>%
# select(diff_en_dias) %>%
# summarise(n=n(),min=min(diff_en_dias), max=max(diff_en_dias), mean=mean(diff_en_dias), median=median(diff_en_dias), p025=quantile(diff_en_dias, .025), p25=quantile(diff_en_dias, .25), p75=quantile(diff_en_dias,.75), p975=quantile(diff_en_dias, .975)) %>% copiar_nombres()
cat("Are gls_materia and cod_delito distinct (differences in the number of categories contained)?")## Are gls_materia and cod_delito distinct (differences in the number of categories contained)?ifelse(base_fiscalia %>%
dplyr::group_by(cod_delito) %>%
summarise(n=n_distinct(gls_materia)) %>%
dplyr::filter(n>1) %>% nrow()>0,"Yes","No")## [1] "No"cat("And viceversa?")## And viceversa?ifelse(base_fiscalia %>%
dplyr::group_by(gls_materia) %>%
summarise(n=n_distinct(cod_delito)) %>%
dplyr::filter(n>1) %>% nrow(), "Yes","No")## [1] "No"cat("Cases in which the same RUC has 2 or more RUTs")## Cases in which the same RUC has 2 or more RUTsformat(base_fiscalia %>%
dplyr::group_by(ruc) %>%
summarise(n=n_distinct(rut_enc_saf)) %>%
dplyr::filter(n>1) %>%
nrow(), big.mark=",")## [1] "6,693"#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
rel_ruc_rut<-
cbind("More than one relationship within a RUC by each RUT",
agrupacion_base %>%
dplyr::filter(max>1) %>% nrow())## Error in dplyr::filter(., max > 1): objeto 'agrupacion_base' no encontradoinvisible("Casos en que agrupados las relaciones por rucs y a su vez por cada rut, resultan tener 2 relaciones")
invisible("ruc=='1101216380-2' tiene todo igual con la otra fila")
invisible("ruc=='1200110540-8' tiene todo igual con la otra fila")
invisible("ruc=='1200685456-5' tiene todo igual con la otra fila")
invisible("ruc=='1200685456-5' tiene todo igual con la otra fila")
# ruc rut_enc_saf min max mean median p025 p25 p75 p975
# 1 1101216380-2 6a72664c6071e9728753279c9e9719e5 2 2 2 2 2 2 2 2
# 2 1200110540-8 471b17aece44998caa6f83fee6b3b483 2 2 2 2 2 2 2 2
# 3 1200685456-5 f1966360761b422b7e826bab2e1a38dc 2 2 2 2 2 2 2 2
# 4 1200771867-3 a2aee30c6c0693a9c13f39e3520f666c 2 2 2 2 2 2 2 2
# 5 1201254538-8 2d3bec94a94304a0b786ab83f7d1021a 2 2 2 2 2 2 2 2
# 6 1300390161-5 f336b9f3e4097cfe736239a8bb937596 1 2 1.5 1.5 1.02 1.25 1.75 1.98
# 7 1600027649-2 bfc39f93fe4e0958c4e0ec16dddec275 2 2 2 2 2 2 2 2
cat("Number of different relationships within a RUC by each RUT")## Number of different relationships within a RUC by each RUTinvisible("ID's de relación por RUCs en RUTs")
agrupacion_base<-
base_fiscalia %>%
group_by(ruc,rut_enc_saf,idrelacion) %>%
summarise(n=n()) %>%
summarise(min=min(n), max=max(n), mean=mean(n, na.rm=T), median=median(n, na.rm=T), p025=quantile(n, .025), p25=quantile(n, .25), p75=quantile(n,.75), p975=quantile(n, .975))## `summarise()` has grouped output by 'ruc', 'rut_enc_saf'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'ruc'. You can override using the `.groups` argument.del_por_rel_ruc<-
cbind(cat="Delitos por relación y RUC",
base_fiscalia %>%
group_by(ruc, idrelacion, gls_materia) %>%
summarise(n=n()) %>%
summarise(min=min(n), max=max(n), mean=mean(n, na.rm=T), median=median(n, na.rm=T), p025=quantile(n, .025), p25=quantile(n, .25), p75=quantile(n,.75), p975=quantile(n, .975)))## `summarise()` has grouped output by 'ruc', 'idrelacion'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'ruc'. You can override using the `.groups` argument.Entries with the same data
options(knitr.kable.NA = '')
#quienes tienen más de uno, son los mismos datos
base_fiscalia %>%
dplyr::filter(idrelacion %in% c(12935546, 13290172, 14124442, 14248963, 14953586, 15559264, 20446792)) %>%
dplyr::arrange(rut_enc_saf, ruc, idrelacion, id_delito) %>%
knitr::kable(size=11, first.strip=T, hide.no="no",
format="html",caption= "Table 6. Cases with duplicated entries",
smd=T, test=T, varLabels=T,noSpaces=T, printToggle=T, dropEqual=F) %>% #,col.names=c("Variables","Residential", "Ambulatory", "p-value")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover","condensed"),font_size= 10) %>%
# kableExtra::add_footnote(c("Note. Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;", "Categorical variables are presented as number (%)"), notation = "none")%>%
kableExtra::scroll_box(width = "100%", height = "375px")| rut_enc_saf | encontrado_como_victima | encontrado_como_imputado | tipo_sujeto_vic | gls_tipo_sujeto_vic | idsujeto_victima | reg | gls_region | idrelacion | ruc | tipo_termino | agrupa_terminos | cod_delito | gls_materia | familia_delito | relacion_vifsaf | cod_parentescoimputado | gls_parentesco | cod_mottermino | gls_mottermino | cod_motsuspension | gls_motsuspension | cod_proctermino | gls_proctermino | idsujeto_imputado | impcod_tiposujeto | gls_tipo_imputado | iddelito | fec_comision | termino_relacion | cod_lugarocurrencia | lugar_ocurrencia | cod_sitiosuceso | gls_sitiosuceso | cod_comunadelito | gls_comuna | cod_region | region_delito | fec_cbiorelacion | medidas_155 | medidas_pp | medidas_ip | marca_suspension_43 | marca_pena_44 | marca_multa_45 | medida_alternativa_46 | clasificacion_pena_47 | tramos_condena_48 | clasificacion_penarpa_1_49 | clasificacion_penarpa_2_50 | marca_suspension_51 | marca_pena_52 | marca_multa_53 | medida_alternativa_54 | clasificacion_pena_55 | tramos_condena_56 | clasificacion_penarpa_1_57 | clasificacion_penarpa_2_58 | fec_comision_simple | fec_cbiorelacion_simple | termino_relacion_simple |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2d3bec94a94304a0b786ab83f7d1021a | SI | NO | 6 | VICTIMA | 41593078 | 5 | 14953586 | 1201254538-8 | SALIDA JUDICIAL | SENTENCIA DEFINITIVA CONDENATORIA | 812 | RECEPTACIÓN. ART. 456 BIS A | OTROS DELITOS CONTRA LA PROPIEDAD | NO | 25 | Sentencia definitiva condenatoria | 0 | 3 | ABREVIADO | 41549070 | 3 | IMPUTADO | 11387704 | 2012-12-18 14:50:00 | 2013-04-16 | 1 | BIENES NACIONALES USO PUBLICO | 1 | VIA PUBLICA URBANA | 310 | VALPARAISO | 5 | 2013-04-16 | SI | NO | SI | SI | Presidio Menor grado mínimo | 2012-12-18 | 2013-04-16 | 2013-04-16 | |||||||||||||||||||
| 2d3bec94a94304a0b786ab83f7d1021a | SI | NO | 6 | VICTIMA | 41593078 | 5 | 14953586 | 1201254538-8 | SALIDA JUDICIAL | SENTENCIA DEFINITIVA CONDENATORIA | 812 | RECEPTACIÓN. ART. 456 BIS A | OTROS DELITOS CONTRA LA PROPIEDAD | NO | 25 | Sentencia definitiva condenatoria | 0 | 3 | ABREVIADO | 41549070 | 3 | IMPUTADO | 11387704 | 2012-12-18 14:50:00 | 2013-04-16 | 1 | BIENES NACIONALES USO PUBLICO | 1 | VIA PUBLICA URBANA | 310 | VALPARAISO | 5 | 2013-04-16 | SI | NO | SI | SI | Presidio Menor grado mínimo | 2012-12-18 | 2013-04-16 | 2013-04-16 | |||||||||||||||||||
| 471b17aece44998caa6f83fee6b3b483 | NO | SI | 6 | VICTIMA | 36800899 | 8 | 13290172 | 1200110540-8 | SALIDA NO JUDICIAL | INCOMPETENCIA | 1099 | OTROS HECHOS | HECHOS DE RELEVANCIA CRIMINAL | NO | 18 | Incomp tratarse materias conoc otro trib | 0 | 0 | SIN PROCEDIMIENTO | 36801001 | 3 | IMPUTADO | 10116153 | 2012-01-30 10:09:00 | 2012-02-06 | 2 | LUGAR HABITADO O DESTINADO A LA HABITACION Y SUS DEPENDENCIAS | 1 | VIVIENDA | 456 | TALCAHUANO | 8 | 2012-02-06 | 2012-01-30 | 2012-02-06 | 2012-02-06 | ||||||||||||||||||||||||
| 471b17aece44998caa6f83fee6b3b483 | NO | SI | 6 | VICTIMA | 36800899 | 8 | 13290172 | 1200110540-8 | SALIDA NO JUDICIAL | INCOMPETENCIA | 1099 | OTROS HECHOS | HECHOS DE RELEVANCIA CRIMINAL | NO | 18 | Incomp tratarse materias conoc otro trib | 0 | 0 | SIN PROCEDIMIENTO | 36801001 | 3 | IMPUTADO | 10116153 | 2012-01-30 10:09:00 | 2012-02-06 | 2 | LUGAR HABITADO O DESTINADO A LA HABITACION Y SUS DEPENDENCIAS | 1 | VIVIENDA | 456 | TALCAHUANO | 8 | 2012-02-06 | 2012-01-30 | 2012-02-06 | 2012-02-06 | ||||||||||||||||||||||||
| 6a72664c6071e9728753279c9e9719e5 | NO | SI | 6 | VICTIMA | 35794461 | 3 | 12935546 | 1101216380-2 | SALIDA JUDICIAL | FACULTAD PARA NO INVESTIGAR | 1099 | OTROS HECHOS | HECHOS DE RELEVANCIA CRIMINAL | NO | 2 | No Inicio Investigación | 0 | 0 | SIN PROCEDIMIENTO | 35794830 | 3 | IMPUTADO | 9848587 | 2011-11-24 10:10:00 | 2011-12-26 | 3 | ESTABLECIMIENTO COMERCIAL | 5 | TIENDA COMERCIAL | 245 | VALLENAR | 3 | 2011-12-26 | 2011-11-24 | 2011-12-26 | 2011-12-26 | ||||||||||||||||||||||||
| 6a72664c6071e9728753279c9e9719e5 | NO | SI | 6 | VICTIMA | 35794461 | 3 | 12935546 | 1101216380-2 | SALIDA JUDICIAL | FACULTAD PARA NO INVESTIGAR | 1099 | OTROS HECHOS | HECHOS DE RELEVANCIA CRIMINAL | NO | 2 | No Inicio Investigación | 0 | 0 | SIN PROCEDIMIENTO | 35794830 | 3 | IMPUTADO | 9848587 | 2011-11-24 10:10:00 | 2011-12-26 | 3 | ESTABLECIMIENTO COMERCIAL | 5 | TIENDA COMERCIAL | 245 | VALLENAR | 3 | 2011-12-26 | 2011-11-24 | 2011-12-26 | 2011-12-26 | ||||||||||||||||||||||||
| a2aee30c6c0693a9c13f39e3520f666c | NO | SI | 6 | VICTIMA | 39546660 | 15 | RM Oriente | 14248963 | 1200771867-3 | SALIDA JUDICIAL | SENTENCIA DEFINITIVA ABSOLUTORIA | 525 | AMENAZAS CONDIC. CONTRA PERSONAS Y PROP. ART. 296 1 y 2, 297 | DELITOS CONTRA LA LIBERTAD E INTIMIDAD DE LAS PERSONAS | SI | 22 | PADRE HIJO COMUN | 24 | Sentencia definitiva absolutoria | 0 | 2 | SIMPLIFICADO | 39546761 | 3 | IMPUTADO | 10847174 | 2012-08-03 12:50:00 | 2013-09-11 | 2 | LUGAR HABITADO O DESTINADO A LA HABITACION Y SUS DEPENDENCIAS | 1 | VIVIENDA | 758 | LA FLORIDA | 15 | RM Oriente | 2013-09-11 | 2012-08-03 | 2013-09-11 | 2013-09-11 | ||||||||||||||||||||
| a2aee30c6c0693a9c13f39e3520f666c | NO | SI | 6 | VICTIMA | 39546660 | 15 | RM Oriente | 14248963 | 1200771867-3 | SALIDA JUDICIAL | SENTENCIA DEFINITIVA ABSOLUTORIA | 525 | AMENAZAS CONDIC. CONTRA PERSONAS Y PROP. ART. 296 1 y 2, 297 | DELITOS CONTRA LA LIBERTAD E INTIMIDAD DE LAS PERSONAS | SI | 22 | PADRE HIJO COMUN | 24 | Sentencia definitiva absolutoria | 0 | 2 | SIMPLIFICADO | 39546761 | 3 | IMPUTADO | 10847174 | 2012-08-03 12:50:00 | 2013-09-11 | 2 | LUGAR HABITADO O DESTINADO A LA HABITACION Y SUS DEPENDENCIAS | 1 | VIVIENDA | 758 | LA FLORIDA | 15 | RM Oriente | 2013-09-11 | 2012-08-03 | 2013-09-11 | 2013-09-11 | ||||||||||||||||||||
| bfc39f93fe4e0958c4e0ec16dddec275 | NO | SI | 6 | VICTIMA | 57866854 | 3 | 20446792 | 1600027649-2 | SALIDA JUDICIAL | SOBRESEIMIENTO DEFINITIVO 240 | 311 | FALSIF LICENCIAS MEDICAS O PENSIONES. ART.202 INC.2° Y 3° | DELITOS CONTRA LA FE PÚBLICA | NO | 9 | S Definit (vencim plazo suspens art 240) | 2 | SUSPENSION CONDICIONAL DEL PROCEDIMIENTO | 1 | ORDINARIO | 57866860 | 3 | IMPUTADO | 15680493 | 2016-01-08 18:14:00 | 2017-07-06 | 3 | ESTABLECIMIENTO COMERCIAL | 8 | ESTABLECIMIENTOS DE ALIMENTACION | 235 | CHAÑARAL | 3 | 2017-07-06 | 2016-01-08 | 2017-07-06 | 2017-07-06 | |||||||||||||||||||||||
| bfc39f93fe4e0958c4e0ec16dddec275 | NO | SI | 6 | VICTIMA | 57866854 | 3 | 20446792 | 1600027649-2 | SALIDA JUDICIAL | SOBRESEIMIENTO DEFINITIVO 240 | 311 | FALSIF LICENCIAS MEDICAS O PENSIONES. ART.202 INC.2° Y 3° | DELITOS CONTRA LA FE PÚBLICA | NO | 9 | S Definit (vencim plazo suspens art 240) | 2 | SUSPENSION CONDICIONAL DEL PROCEDIMIENTO | 1 | ORDINARIO | 57866860 | 3 | IMPUTADO | 15680493 | 2016-01-08 18:14:00 | 2017-07-06 | 3 | ESTABLECIMIENTO COMERCIAL | 8 | ESTABLECIMIENTOS DE ALIMENTACION | 235 | CHAÑARAL | 3 | 2017-07-06 | 2016-01-08 | 2017-07-06 | 2017-07-06 | |||||||||||||||||||||||
| f1966360761b422b7e826bab2e1a38dc | SI | NO | 6 | VICTIMA | 39189042 | 7 | 14124442 | 1200685456-5 | SALIDA NO JUDICIAL | ARCHIVO PROVISIONAL | 856 | APROPIACION INDEBIDA ART.470 N°1 | DELITOS ECONÓMICOS Y TRIBUTARIOS | NO | 1 | Archivo Provisional | 0 | 0 | SIN PROCEDIMIENTO | 39189110 | 3 | IMPUTADO | 10751252 | 2012-07-09 16:12:00 | 2014-06-09 | 0 | 0 | 390 | TALCA | 7 | 2014-06-09 | 2012-07-09 | 2014-06-09 | 2014-06-09 | ||||||||||||||||||||||||||
| f1966360761b422b7e826bab2e1a38dc | SI | NO | 6 | VICTIMA | 39189042 | 7 | 14124442 | 1200685456-5 | SALIDA NO JUDICIAL | ARCHIVO PROVISIONAL | 856 | APROPIACION INDEBIDA ART.470 N°1 | DELITOS ECONÓMICOS Y TRIBUTARIOS | NO | 1 | Archivo Provisional | 0 | 0 | SIN PROCEDIMIENTO | 39189110 | 3 | IMPUTADO | 10751252 | 2012-07-09 16:12:00 | 2014-06-09 | 0 | 0 | 390 | TALCA | 7 | 2014-06-09 | 2012-07-09 | 2014-06-09 | 2014-06-09 | ||||||||||||||||||||||||||
| f336b9f3e4097cfe736239a8bb937596 | NO | SI | 6 | VICTIMA | 43358250 | 7 | 15559264 | 1300390161-5 | SALIDA JUDICIAL | FACULTAD PARA NO INVESTIGAR | 524 | AMENAZAS SIMPLES CONTRA PERSONAS Y PROPIEDADES ART. 296 Nº3. | DELITOS CONTRA LA LIBERTAD E INTIMIDAD DE LAS PERSONAS | SI | 21 | EX CONVIVIENTE | 2 | No Inicio Investigación | 0 | 0 | SIN PROCEDIMIENTO | 43358179 | 3 | IMPUTADO | 11856719 | 2013-04-18 12:23:00 | 2013-04-19 | 2 | LUGAR HABITADO O DESTINADO A LA HABITACION Y SUS DEPENDENCIAS | 1 | VIVIENDA | 390 | TALCA | 7 | 2013-04-19 | 2013-04-18 | 2013-04-19 | 2013-04-19 | ||||||||||||||||||||||
| f336b9f3e4097cfe736239a8bb937596 | NO | SI | 6 | VICTIMA | 43358250 | 7 | 15559264 | 1300390161-5 | SALIDA JUDICIAL | FACULTAD PARA NO INVESTIGAR | 524 | AMENAZAS SIMPLES CONTRA PERSONAS Y PROPIEDADES ART. 296 Nº3. | DELITOS CONTRA LA LIBERTAD E INTIMIDAD DE LAS PERSONAS | SI | 21 | EX CONVIVIENTE | 2 | No Inicio Investigación | 0 | 0 | SIN PROCEDIMIENTO | 43358179 | 3 | IMPUTADO | 11856719 | 2013-04-18 12:23:00 | 2013-04-19 | 2 | LUGAR HABITADO O DESTINADO A LA HABITACION Y SUS DEPENDENCIAS | 1 | VIVIENDA | 390 | TALCA | 7 | 2013-04-19 | 2013-04-18 | 2013-04-19 | 2013-04-19 |
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
lca_entropia <- function(x,seed,k,f,dat,nbr_repet,na_rm){
#x=texto distintivo
#y =seed ej: 4345
#k= cuantos modelos se van a calcular
#f= función
#dat= datos
#nbr_repet= número de repeeticiones
#na_rm= permitir valores perdidos
max_II <- -10000000
min_bic <- 10000000
set.seed(seed)
assign(paste("lc", 1, "_", x, sep = ""),#get(paste("lc", i, "_", x, sep = ""))
poLCA(f, dat, nclass=1, maxiter=5000,
tol=1e-5, na.rm=na_rm,
nrep=nbr_repet, verbose=F, calc.se=TRUE)
)
set.seed(seed)
for(i in 2:k){
assign(paste("lc", i, "_", x, sep = ""),#get(paste("lc", i, "_", x, sep = ""))
poLCA(f, dat, nclass=i, maxiter=5000,
tol=1e-5, na.rm=na_rm,
nrep=nbr_repet, verbose=F, calc.se=TRUE)
)
if(get(paste("lc",i,"_",x,sep=""))$bic < min_bic){
min_bic <- get(paste("lc",i,"_",x,sep=""))$bic
LCA_best_model<-get(paste("lc",i,"_",x,sep=""))
}
}
tab.modfit<-data.frame(matrix(rep(999,14),nrow=1))
names(tab.modfit)<-c("log-likelihood","Chi2","Chi2_pval",
"resid. df","BIC",
"aBIC","cAIC","likelihood-ratio","LLik_pval","Entropy", "Entropy.R2","Dev Change","df","pval")
relative.entropy<-function(lc){
en<--sum(lc$posterior*
log(lc$posterior),na.rm=T)
e<-1-en/(nrow(lc$posterior)*log(ncol(lc$posterior)))
return(e)
}
machine_tolerance <- sqrt(.Machine$double.eps)
entropy.R2 <- function(fit) {
entropy <- function(p) {
p <- p[p > machine_tolerance] # since Lim_{p->0} p log(p) = 0
sum(-p * log(p))
}
error_prior <- entropy(fit$P) # Class proportions
error_post <- mean(apply(fit$posterior, 1, entropy))
R2_entropy <- (error_prior - error_post) / error_prior
R2_entropy
}
for(i in 2:k){
tab.modfit<-rbind(tab.modfit,
c(get(paste("lc",i,"_",x,sep=""))$llik,
get(paste("lc",i,"_",x,sep=""))$Chisq,
get(paste("lc",i,"_",x,sep=""))$Chisq.pvalue,
get(paste("lc",i,"_",x,sep=""))$resid.df,
get(paste("lc",i,"_",x,sep=""))$bic,
(-2*get(paste("lc",i,"_",x,sep=""))$llik) +
((log((get(paste("lc",i,"_",x,sep=""))$N + 2)/24)) *
get(paste("lc",i,"_",x,sep=""))$npar),
(-2*get(paste("lc",i,"_",x,sep=""))$llik) +
get(paste("lc",i,"_",x,sep=""))$npar *
(1 + log(get(paste("lc",i,"_",x,sep=""))$N)),
get(paste("lc",i,"_",x,sep=""))$Gsq,
get(paste("lc",i,"_",x,sep=""))$Gsq.pvalue,
relative.entropy(get(paste("lc",i,"_",x,sep=""))),
entropy.R2(get(paste("lc",i,"_",x,sep=""))),
(get(paste("lc",i-1,"_",x,sep=""))$Gsq- get(paste("lc",i,"_",x,sep=""))$Gsq),
(get(paste("lc",i-1,"_",x,sep=""))$resid.df- get(paste("lc",i,"_",x,sep=""))$resid.df),
(pchisq( get(paste("lc",i-1,"_",x,sep=""))$Gsq- get(paste("lc",i,"_",x,sep=""))$Gsq, get(paste("lc",i-1,"_",x,sep=""))$resid.df- get(paste("lc",i,"_",x,sep=""))$resid.df))
))
}
tab.modfit<-round(tab.modfit[-1,],2)
tab.modfit$Nclass<-2:k
newList <- list("lc_entropy_table" = tab.modfit,
"lc_entropy_best_model" = LCA_best_model)
return(list2env(newList ,.GlobalEnv))
}
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#' Bivariate residuals for latent class models
#'
#' Calculate the "bivariate residuals" (BVRs) between pairs of variables
#' in a latent class model.
#'
#' This function compares the model-implied (expected) counts in the crosstables
#' of all pairs of observed dependent variables to the observed counts. For each
#' pair, it calculates a "chi-square" statistic,
#'
#' \deqn{\text{BVR} = \sum_{j, j'} \frac{(n_{jj'} - e_{jj'})^2}{e_{jj'}}},
#'
#' where \eqn{n_{jj'}} are the observed counts for categories \eqn{j} and \eqn{j'}
#' of the variables being crosstabulated, and \eqn{e_{jj'}} are
#' the expected counts under the latent class model.
#'
#' Note that the BVR does not follow an asymptotic chi-square distribution and
#' for accurate p-values, parametric bootstrapping is necessary (Oberski et al. 2013).
#'
#' @param fit A poLCA fit object
#' @param tol Optional: tolerance for small expected counts
#' @param rescale_to_df Optional: whether to divide the pairwise "chi-square" values by
#' the degrees of freedom of the local crosstable. Default is TRUE.
#' @return The table of bivariate residuals
#' @author Daniel Oberski (daniel.oberski@gmail.com)
#' @seealso \code{\link{poLCA}} for fitting the latent class model.
#' @references
#' Oberski, DL, Van Kollenburg, GH and Vermunt, JK (2013).
#' A Monte Carlo evaluation of three methods to detect local dependence in binary data latent class models.
#' Advances in Data Analysis and Classification 7 (3), 267-279.
#' @examples
#' data(values)
#' f <- cbind(A, B, C, D) ~ 1
#' M0 <- poLCA(f,values, nclass=1, verbose = FALSE)
#' bvr(M0) # 12.4, 5.7, 8.3, 15.6, ...
bvr <- function(fit, tol = 1e-3, rescale_to_df = TRUE) {
stopifnot(class(fit) == "poLCA")
ov_names <- names(fit$predcell)[1:(ncol(fit$predcell) - 2)]
ov_combn <- combn(ov_names, 2)
get_bvr <- function(ov_pair) {
form_obs <- as.formula(paste0("observed ~ ", ov_pair[1], " + ", ov_pair[2]))
form_exp <- as.formula(paste0("expected ~ ", ov_pair[1], " + ", ov_pair[2]))
counts_obs <- xtabs(form_obs, data = fit$predcell)
counts_exp <- xtabs(form_exp, data = fit$predcell)
counts_exp <- ifelse(counts_exp < tol, tol, counts_exp) # Prevent Inf/NaN
bvr_df <- prod(dim(counts_exp) - 1)
bvr_value <- sum((counts_obs - counts_exp)^2 / counts_exp)
if(rescale_to_df) bvr_value <- bvr_value / bvr_df
attr(bvr_value, "df") <- bvr_df
bvr_value
}
bvr_pairs <- apply(ov_combn, 2, get_bvr)
attr(bvr_pairs, "rescale_to_df") <- rescale_to_df
attr(bvr_pairs, "class") <- "dist"
attr(bvr_pairs, "Size") <- length(ov_names)
attr(bvr_pairs, "Labels") <- ov_names
attr(bvr_pairs, "Diag") <- FALSE
attr(bvr_pairs, "Upper") <- FALSE
bvr_pairs
}
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
poLCA.entropy.fix <- function (lc)
{
K.j <- sapply(lc$probs, ncol)
fullcell <- expand.grid(sapply(K.j, seq, from = 1))
P.c <- poLCA.predcell(lc, fullcell)
return(-sum(P.c * log(P.c), na.rm = TRUE))
}
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#Calculate entropy R2 for poLCA model
# MIT license
# Author: Daniel Oberski
# Input: result of a poLCA model fit
# Output: entropy R^2 statistic (Vermunt & Magidson, 2013, p. 71)
# See: daob.nl/wp-content/uploads/2015/07/ESRA-course-slides.pdf
# And: https://www.statisticalinnovations.com/wp-content/uploads/LGtecnical.pdf
#https://gist.github.com/daob/c2b6d83815ddd57cde3cebfdc2c267b3
machine_tolerance <- sqrt(.Machine$double.eps)
entropy.R2 <- function(fit) {
entropy <- function(p) {
p <- p[p > machine_tolerance] # since Lim_{p->0} p log(p) = 0
sum(-p * log(p))
}
error_prior <- entropy(fit$P) # Class proportions
error_post <- mean(apply(fit$posterior, 1, entropy))
R2_entropy <- (error_prior - error_post) / error_prior
R2_entropy
}
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#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#http://researchdata.gla.ac.uk/879/1/Survey_data_processed_using_R.pdf
##Function to plot variable probabilites by latent class
## Function to undertake chisquare analayis and plot graphs of residuals and contributions
chisquaretest.predictions.function <-
function(indfactor.data,
predclass.data,
noclasses,
pitem,
gitem,
chirows,
chicols) {
chisquare.results <- chisq.test(indfactor.data, predclass.data)
residuals.data <- chisquare.results$residuals
colnames(residuals.data) <- chicols
rownames(residuals.data) <- chirows
title.text <-
paste(
"Residuals: chi Square Crosstabulation of\n",
pitem,
"and",
gitem,
"\n(Chisquare =",
round(chisquare.results$statistic, 3),
" p <",
round(chisquare.results$p.value, 3),
")",
sep = " "
)
corrplot(
residuals.data,
is.cor = FALSE,
title = title.text,
mar = c(0, 0, 4, 0)
)
contrib.data <-
100 * residuals.data ^ 2 / chisquare.results$statistic
round(contrib.data, 3)
colnames(contrib.data) <- chicols
rownames(contrib.data) <- chirows
title.text <-
title.text <-
paste(
"Contributions: chi Square Crosstabulation of\n",
pitem,
"and",
gitem,
"\n(Chisquare =",
round(chisquare.results$statistic, 3),
" p <",
round(chisquare.results$p.value, 3),
")",
sep = " "
)
corrplot(
contrib.data,
is.cor = FALSE,
title = title.text,
mar = c(0, 0, 4, 0)
)
return(chisquare.results)
}
##Funciton for Cramers V test
cv.test = function(x, y) {
CV = sqrt(chisq.test(x, y, correct = FALSE)$statistic /
(length(x) * (min(
length(unique(x)), length(unique(y))
) - 1)))
print.noquote("Cramér V / Phi:")
return(as.numeric(CV))
}mydata <-
base_fiscalia %>%
dplyr::mutate(accused=dplyr::case_when(encontrado_como_victima=="SI" & encontrado_como_imputado=="SI"~"IMPUTADO",
encontrado_como_victima=="NO" & encontrado_como_imputado=="SI"~"IMPUTADO",
T~"VICTIMA")) %>%
dplyr::select("accused","gls_tipo_sujeto_vic","gls_region","agrupa_terminos","familia_delito", "relacion_vifsaf","gls_parentesco", "gls_mottermino", "gls_motsuspension", "gls_proctermino", "gls_tipo_imputado", "lugar_ocurrencia", "region_delito", "medidas_155", "clasificacion_pena_47") %>%
dplyr::mutate(across(, ~ifelse(is.na(.),9999,as.numeric(factor(.)))))
f_df<-with(mydata, cbind(accused,gls_tipo_sujeto_vic,gls_region,agrupa_terminos,familia_delito, relacion_vifsaf,gls_parentesco, gls_mottermino, gls_motsuspension, gls_proctermino, gls_tipo_imputado, lugar_ocurrencia, region_delito)~1)#, medidas_155, clasificacion_pena_47# f is the selected variables
# dat is the data
# nb_var is the number of selected variables
# k is the number of latent class generated
# nbr_repet is the number of repetition to
# reach the convergence of EM algorithm
# x es el código para las variables de los modelos
#seed es el numero random para las semillas. ej: 4345.
#Modo de calcular el mejor modelo.
old <- Sys.time()
library(poLCA)
lca_entropia(x="primer_lca",seed = 4345,k=5,f=f_df, dat=mydata, nbr_repet = 5,na_rm=T) #clus_iter
#Error: cannot allocate vector of size 42.3 Gb
#lca_select(f_naq, mydata_naq,k=10,nbr_repet=clus_iter, x="naq", seed=4345)
#
new<-(Sys.time())
warning("Time taken in process: ");new-old
# ALERT: at least one manifest variable contained only one
# outcome category, and has been removed from the analysis.
#
# Error: cannot allocate vector of size 28.1 Gb
#Within poLCA, parameter estimates are obtained by a procedure that repeatedly improves estimates.
#This is stopped when no further improvements are obtained, or until a maximum number of iterations is reached. The starting values are the values at which such repetitions were started. Increasing the number 4 R. ACHTERHOF ET AL.of iterations (cycles within each estimation) and setting more different starting values for each repetition results in a greater likelihood that the global (rather than local) maximum of the log-likelihood function (and thus, the best possible solution) is reached. The maximum number of iterations was chosen as 10.000, and 500 different sets of starting values were used (thus going beyond the recommendations by Linzer & Lewis, 2011; Oberski, 2016). As such, the influence of chance was minimized while the reproducibility of the results was maximized.#Si probs.start se establece en NULL (predeterminado) al llamar Polca, a continuación, la función genera los valores de partida al azar en cada ejecución. Esto significa que repite carreras de Polca normalmente producirán resultados con los mismos parámetros estimados (correspondiente a la misma el máximo diario de probabilidad), pero con etiquetas de clase latentes reordenados
lc_entropy_best_model_lca<-lc_entropy_best_model
lc_entropy_table_lca<-lc_entropy_table
#A list of matrices of class-conditional response probabilities to be used as the starting values for the estimation algorithm. Each matrix in the list corresponds to one manifest variable, with one row for each latent class, and one column for each outcome. The default is NULL, producing random starting values. Note that if nrep>1, then any user-specified probs.start values are only used in the first of the nrep attempts.
#The poLCA.reorder function takes as its first argument the list of starting values probs.start, and as its second argument a vector describing the desired reordering of the latent classes.
new.probs.start <- poLCA.reorder(lc_entropy_best_model_lca$probs.start, order(lc_entropy_best_model_lca$P, decreasing = T))
#new.probs.start <-poLCA.reorder(probs.start,c(4,1,3,2))
#A continuación, ejecute PoLCA, una vez más, esta vez utilizando los valores iniciales reordenados en la llamada de función.
#The argument nrep=5 tells the program to repeat nrep times, and keep the fit with the highest likelihood to try to avoid local maxima.
#.maxiter – The maximum number of iterations through which the estimation algorithm will cycle.
#.nrep - Number of times to estimate the model, using different values of probs.start. (default is one)
set.seed(4345)
LCA_best_model_final<-
poLCA(f_df, mydata, nclass=length(lc_entropy_best_model_lca$P), maxiter=10000,tol=1e-15, na.rm=FALSE,nrep=clus_iter, verbose=TRUE, calc.se=TRUE,probs.start=new.probs.start)
output_LCA_best_model_final<-capture.output(LCA_best_model_final)
glance_LCA_best_model_final<-glance(LCA_best_model_final)
surveymonkey_accionsalududp_df2_cor_table2_LCA <- broom::augment(LCA_best_model_final, data = surveymonkey_accionsalududp_df2_cor_table2)## If you are interested in the population-shares of the classes, you can get them like this:
invisible(round(colMeans(LCA_best_model_final$posterior)*100,2))
## or you inspect the estimated class memberships:
invisible(round(prop.table(table(LCA_best_model_final$predclass)),4)*100)
lcmodel <- reshape2::melt(LCA_best_model_final$probs, level=2)
lcmodel2<-
lcmodel %>%
dplyr::mutate(L2=factor(L2, levels=c("tamizaje_ans", "tamizaje_dep","p8_exp_c19_cerca_cont_rec",paste0("p12_13_nec_insuf_0",c(1,2,3,4,6,7,9)),paste0("p40_cond_trab_istas_0",6:9),"p45_demo_sexo"),labels=c("ansiedad","depresion","contact_covid",paste0("nec_insuf_",c("gu","po","pf","ov","mq","m95","de")),paste0("istas_",c("sup_esc","sup_ayu","comp_ayu","amb_comp")),"sexo"), ordered=T)) %>%
dplyr::mutate(Var2 = factor(dplyr::case_when(as.character(Var2)=="Pr(6)"~"Pr miss",
TRUE~as.character(Var2))))
zp1 <- ggplot(lcmodel2,aes(x = L2, y = value, fill = Var2))
zp1 <- zp1 + geom_bar(stat = "identity", position = "stack")
zp1 <- zp1 + facet_grid(Var1 ~ .)
zp1 <- zp1 + scale_fill_brewer(type="seq", palette="Greys", na.value = "white") +theme_bw()
zp1 <- zp1 + labs(y = "Percentage of Probabilities of Response",
x = "Items",
fill ="Cateorías de\nRespuesta")
zp1 <- zp1 + theme( axis.text.y=element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
axis.ticks.y=element_blank(),
panel.grid.major.y=element_blank())
zp1 <- zp1 + guides(fill = guide_legend(reverse=TRUE))
print(zp1)
jpeg("_Fig_LCA.jpg", height = 7, width = 11.5, units = 'in', res = 600)
zp1+ theme(plot.background = element_rect(fill=NA, color = NA))
dev.off()#In this case, residuals are actual cell counts vs. expected cell counts.
bvr_LCA_best_model<-bvr(LCA_best_model_final)
#conditional probabilities
#Pr(B1=1|Class 3)
posteriors <- data.frame(LCA_best_model_final$posterior, predclass=LCA_best_model_final$predclass)
library(plyr)
classification_table <- plyr::ddply(posteriors, ~predclass, function(x) colSums(x[,1:4]))
clasification_errors<- 1 - sum(diag(as.matrix(classification_table[,2:5]))) / sum(classification_table[,2:5])
paste0("Error de clasificación: ", scales::percent(as.numeric(clasification_errors),accuracy=.01))
detach("package:plyr", unload=TRUE)
#\#minimum average posterior robability of cluster membership (\>0.7), interpretability (classes are clearly distinguishable), and parsimony (each class has a sufficient sample size for further analysis; n≥50).Ver si la exclusión de casos que no calzan en alguna clase tiene consecuencias.
to_string <- as_labeller(c(`0` = "Sig. Outcomes", `1` = "Non Sig. Outcomes"))
manualcolors<-c('indianred1','cornflowerblue', 'gray50', 'darkolivegreen4', 'slateblue2',
'firebrick4', 'goldenrod4')
#"#FF0000" "#00A08A" "#F2AD00" "#F98400" "#5BBCD6"
#"#798E87" "#C27D38" "#CCC591" "#29211F"
lc_entropy_table%>%
#dplyr::mutate_at(.vars=vars(Gsq, Llik, AIC, mAIC, AICc, HT, cAIC, AICu, BIC, aBIC, HQ), .funs = funs(`zw`=scale(.)))%>%
tidyr::pivot_longer(cols = -Nclass,names_to="indices", values_to="value",values_drop_na = F)%>%
#dplyr::group_by(Nclass)%>%
dplyr::mutate(indices=factor(indices, levels=c('resid. df', 'likelihood-ratio', 'log-likelihood',"LLik_pval",'Chi2', 'Chi2_pval', 'BIC', 'aBIC', 'cAIC',"Entropy","Entropy.R2","Dev Change","df","pval"),labels=c('Degrees of Freedom', "Deviance","Log-Likelihood",'p value','Chi2','p value','Akaike Information\nCriterion',"Bayesian Information\nCriterion (BIC)","Adjusted BIC","Entropy","Entropy R2", "Deviance Change\n(with previous model)", "df", "pval of diffs"))) %>%
dplyr::filter(indices %in% c('Akaike Information\nCriterion',"Bayesian Information\nCriterion (BIC)","Adjusted BIC")) %>%
dplyr::mutate(Nclass=factor(Nclass)) %>%
ggplot(aes(x=Nclass,y=value,color=indices, group=indices))+
geom_line()+
geom_point()+
theme_classic()+
labs(x="Number of Classes", y="Value")+
labs(color = "Indices")+
geom_vline(xintercept=3)+
labs(caption="Vertical line= Selected model")
#,'Gsq_zw', 'Llik_zw', 'AIC_zw', 'mAIC_zw', 'AICc_zw', 'HT_zw', 'cAIC_zw', 'AICu_zw', 'BIC_zw', 'aBIC_zw', 'HQ_zw'
#lca_selectnaq %>% dplyr::arrange(BIC)
#International Journal of Workplace Health Management (Zhang et al., 2018).
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
jpeg("_Fig_lc_entropy_table3.jpg", height = 7, width = 11.5, units = 'in', res = 600)
lc_entropy_table%>%
#dplyr::mutate_at(.vars=vars(Gsq, Llik, AIC, mAIC, AICc, HT, cAIC, AICu, BIC, aBIC, HQ), .funs = funs(`zw`=scale(.)))%>%
tidyr::pivot_longer(cols = -Nclass,names_to="indices", values_to="value",values_drop_na = F)%>%
#dplyr::group_by(Nclass)%>%
dplyr::mutate(indices=factor(indices, levels=c('resid. df', 'likelihood-ratio', 'log-likelihood',"LLik_pval",'Chi2', 'Chi2_pval', 'BIC', 'aBIC', 'cAIC',"Entropy","Entropy.R2","Dev Change","df","pval"),labels=c('Degrees of Freedom', "Deviance","Log-Likelihood",'p value','Chi2','p value','Akaike Information\nCriterion',"Bayesian Information\nCriterion (BIC)","Adjusted BIC","Entropy","Entropy R2", "Deviance Change\n(with previous model)", "df", "pval of diffs"))) %>%
dplyr::filter(indices %in% c('Akaike Information\nCriterion',"Bayesian Information\nCriterion (BIC)","Adjusted BIC")) %>%
dplyr::mutate(Nclass=factor(Nclass)) %>%
ggplot(aes(x=Nclass,y=value,color=indices, group=indices))+
geom_line()+
geom_point()+
theme_classic()+
labs(x="Number of Classes", y="Value")+
labs(color = "Indices")+
geom_vline(xintercept=3)+
labs(caption="Vertical line= Selected model")
dev.off()# set.seed(2017)
# # Fit the Poisson mixture model
# library(flexmix)
# poisson_mm <- stepFlexmix(as.matrix(mydata) ~ 1,
# k = 1:15,
# nrep = 5,
# model = FLXMCmvpois(),
# control = list(tolerance = 1e-15, iter.max = 1000))
#
#
#
# print(plot(poisson_mm, mark = 1, col = "grey", markcol = 1))
#
#
# table(clusters(poisson_mm@models$`11`))
ggcormat(
data=,
type="np",
partial=F,
output="dataframe"
) %>%
dplyr::mutate_if(is.numeric, ~round(.,2))%>%
flextable()Merge with SENDAs database
invisible("¿Hay RUTs de fiscalía que no existen en la base de datos SENDA?")
invisible("Ver cuántos calzan con la Base anterior (datos descartados en etapas del proceso de limpieza de datos)")
rbind(
cbind(cat="RUTs in P.O. that are not in SENDA's most recent database (accused)",
#Sólo el recuento de los RUTs
base_fiscalia_acc_imp %>%
#sólo dejo RUT únicos en la de septiembre, cosa que no me aumenten las filas originales
dplyr::left_join(distinct(CONS_C1_df_dup_SEP_2020[,c("hash_key","dup")],hash_key, .keep_all = T),by=c("rut_enc_saf"="hash_key")) %>%
dplyr::distinct(rut_enc_saf, .keep_all = T) %>%
dplyr::summarise(n=n(),
sum_na = length(n[is.na(dup)]),
perc = scales::percent(sum_na/n, accuracy=.01))),
cbind(cat="RUTs in P.O. that are not in SENDA's most recent database (victims)",
base_fiscalia_acc_vic %>%
#sólo dejo RUT únicos en la de septiembre, cosa que no me aumenten las filas originales
dplyr::left_join(distinct(CONS_C1_df_dup_SEP_2020[,c("hash_key","dup")],hash_key, .keep_all = T),by=c("rut_enc_saf"="hash_key")) %>%
dplyr::distinct(rut_enc_saf, .keep_all = T) %>%
dplyr::summarise(n=n(),
sum_na = length(n[is.na(dup)]),
perc = scales::percent(sum_na/n, accuracy=.01))),
cbind(cat="RUTs that matches with the original SENDA's database (accused)",
#Sólo el recuento de los RUTs
base_fiscalia_acc_imp %>%
#sólo dejo RUT únicos en la de septiembre, cosa que no me aumenten las filas originales
dplyr::left_join(distinct(CONS_C1[,c("HASH_KEY","row")],HASH_KEY, .keep_all = T),by=c("rut_enc_saf"="HASH_KEY")) %>%
dplyr::distinct(rut_enc_saf, .keep_all = T) %>%
dplyr::summarise(n=n(),
sum_na = length(n[is.na(row)]),
perc = scales::percent(sum_na/n, accuracy=.01))),
cbind(cat="RUTs that matches with the original SENDA's database (victims)",
base_fiscalia_acc_vic %>%
#sólo dejo RUT únicos en la de septiembre, cosa que no me aumenten las filas originales
dplyr::left_join(distinct(CONS_C1[,c("HASH_KEY","row")],HASH_KEY, .keep_all = T),by=c("rut_enc_saf"="HASH_KEY")) %>%
dplyr::distinct(rut_enc_saf, .keep_all = T) %>%
dplyr::summarise(n=n(),
sum_na = length(n[is.na(row)]),
perc = scales::percent(sum_na/n, accuracy=.01)))
) %>%
knitr::kable(size=11, first.strip=T, hide.no="no",
format="html",caption= "Table 7. Comparison of availability of RUTs") %>% #,col.names=c("Variables","Residential", "Ambulatory", "p-value")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover","condensed"),font_size= 12)| cat | n | sum_na | perc |
|---|---|---|---|
| RUTs in P.O. that are not in SENDA’s most recent database (accused) | 63537 | 369 | 0.58% |
| RUTs in P.O. that are not in SENDA’s most recent database (victims) | 57320 | 398 | 0.69% |
| RUTs that matches with the original SENDA’s database (accused) | 63537 | 0 | 0.00% |
| RUTs that matches with the original SENDA’s database (victims) | 57320 | 0 | 0.00% |
Codebook
knitr::opts_chunk$set(
warning = TRUE, # show warnings during codebook generation
message = TRUE, # show messages during codebook generation
error = TRUE, # do not interrupt codebook generation in case of errors,
# usually better for debugging
echo = TRUE # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())
pander::panderOptions("table.split.table", Inf)
if(!require(codebook)){install.packages("codebook")}
if(!require(future)){install.packages("future")}## Loading required package: future## Warning: package 'future' was built under R version 4.0.5##
## Attaching package: 'future'## The following object is masked from 'package:ggstatsplot':
##
## %<-%## The following object is masked from 'package:caret':
##
## cluster## The following objects are masked from 'package:igraph':
##
## %->%, %<-%## The following object is masked from 'package:survival':
##
## clusterif(!require(dplyr)){install.packages("dplyr")}codebook_data <- dplyr::select(base_fiscalia, -c("rut_enc_saf","ruc","idrelacion","cod_delito","cod_lugarocurrencia","cod_parentescoimputado","cod_mottermino","cod_motsuspension","cod_proctermino","idsujeto_imputado","impcod_tiposujeto","cod_lugarocurrencia","cod_sitiosuceso","cod_comunadelito","cod_region"))
muestra=0
if(muestra==1){
# omit the following lines, if your missing values are already properly labelled
codebook_data <- detect_missing(codebook_data,
only_labelled = TRUE, # only labelled values are autodetected as
# missing
negative_values_are_missing = FALSE, # negative values are missing values
ninety_nine_problems = FALSE, # 99/999 are missing values, if they
# are more than 5 MAD from the median
)
}
# If you are not using formr, the codebook package needs to guess which items
# form a scale. The following line finds item aggregates with names like this:
# scale = scale_1 + scale_2R + scale_3R
# identifying these aggregates allows the codebook function to
# automatically compute reliabilities.
# However, it will not reverse items automatically.
#codebook_data <- detect_scales(codebook_data)In the following Tables and graphics, there is a summary of the variables and documentation of their characteristics. This high-level summary may permit us find errors on coding, help us to understand missingness and guide us towards an objective, or lead us to more questions. If a variable contains the symbol (*), it means that this variable has a concatenation of values among continuous entries in treatments.
metadata(codebook_data)$name <- "Prosecutor Support System (SAF), database of the Public Prosecutor's Office."
metadata(codebook_data)$description <- "The information was extracted and processed from the Prosecutor Support System (SAF) database in accordance with the criteria established in the methodological document 'Criteria for Extracting Information from the SAF - 11_04_2018', prepared and updated in 2018 by the Studies, Evaluation, Control and Management Development Division. Data obtained at 2021-08-13; Period= 2010 to 2019; Unit of measurement= Relationships; Offenses considered= All; Date to consider= Date of termination of the case; National Coverage; Detail or summary= Detail; In the search for victims, only direct victims were considered; The RUTs indicated are searched only in cases terminated or suspended in the period 2010 - 2019;
The following types of accused are considered: COMPLAINED, ACCUSED, QUERELLED, WITNESS, SUSPECTED AND INVESTIGATED"
codebook(codebook_data)Metadata
Description
Dataset name: Prosecutor Support System (SAF), database of the Public Prosecutor’s Office.
The information was extracted and processed from the Prosecutor Support System (SAF) database in accordance with the criteria established in the methodological document ‘Criteria for Extracting Information from the SAF - 11_04_2018’, prepared and updated in 2018 by the Studies, Evaluation, Control and Management Development Division. Data obtained at 2021-08-13; Period= 2010 to 2019; Unit of measurement= Relationships; Offenses considered= All; Date to consider= Date of termination of the case; National Coverage; Detail or summary= Detail; In the search for victims, only direct victims were considered; The RUTs indicated are searched only in cases terminated or suspended in the period 2010 - 2019; The following types of accused are considered: COMPLAINED, ACCUSED, QUERELLED, WITNESS, SUSPECTED AND INVESTIGATED
Metadata for search engines
- Date published: 2021-11-16
|
#Variables
encontrado_como_victima
Distribution
Distribution of values for encontrado_como_victima
0 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| encontrado_como_victima | character | 0 | 1 | 2 | 0 | 2 | 2 | 0 |
encontrado_como_imputado
Distribution
Distribution of values for encontrado_como_imputado
0 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| encontrado_como_imputado | character | 0 | 1 | 2 | 0 | 2 | 2 | 0 |
tipo_sujeto_vic
Distribution
Distribution of values for tipo_sujeto_vic
18053 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| tipo_sujeto_vic | character | 18053 | 0.9682332 | 8 | 0 | 1 | 2 | 0 |
gls_tipo_sujeto_vic
Distribution
Distribution of values for gls_tipo_sujeto_vic
18053 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| gls_tipo_sujeto_vic | character | 18053 | 0.9682332 | 8 | 0 | 7 | 19 | 0 |
idsujeto_victima
Distribution
Distribution of values for idsujeto_victima
0 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | label |
|---|---|---|---|---|---|---|---|---|---|---|
| idsujeto_victima | numeric | 0 | 1 | 0 | 48183970 | 84105717 | 47784904 | 17843954 | <U+2581><U+2585><U+2587><U+2587><U+2585> |
reg
Distribution
Distribution of values for reg
0 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | label |
|---|---|---|---|---|---|---|---|---|---|---|
| reg | numeric | 0 | 1 | 1 | 11 | 20 | 10.69546 | 5.481066 | <U+2583><U+2587><U+2582><U+2587><U+2583> |
gls_region
Distribution
Distribution of values for gls_region
317781 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| gls_region | character | 317781 | 0.4408188 | 4 | 0 | 6 | 15 | 0 |
tipo_termino
Distribution
Distribution of values for tipo_termino
21 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| tipo_termino | character | 21 | 0.999963 | 3 | 0 | 14 | 18 | 0 |
agrupa_terminos
Distribution
Distribution of values for agrupa_terminos
21 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| agrupa_terminos | character | 21 | 0.999963 | 16 | 0 | 13 | 40 | 0 |
gls_materia
Distribution
Distribution of values for gls_materia
0 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| gls_materia | character | 0 | 1 | 354 | 0 | 7 | 60 | 0 |
familia_delito
Distribution
Distribution of values for familia_delito
0 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| familia_delito | character | 0 | 1 | 21 | 0 | 5 | 60 | 0 |
relacion_vifsaf
Distribution
Distribution of values for relacion_vifsaf
0 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| relacion_vifsaf | character | 0 | 1 | 2 | 0 | 2 | 2 | 0 |
gls_parentesco
Distribution
Distribution of values for gls_parentesco
452460 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| gls_parentesco | character | 452460 | 0.2038318 | 25 | 0 | 5 | 28 | 0 |
gls_mottermino
Distribution
Distribution of values for gls_mottermino
10353 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| gls_mottermino | character | 10353 | 0.9817824 | 25 | 0 | 8 | 40 | 0 |
gls_motsuspension
Distribution
Distribution of values for gls_motsuspension
493206 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| gls_motsuspension | character | 493206 | 0.1321334 | 5 | 0 | 19 | 40 | 0 |
gls_proctermino
Distribution
Distribution of values for gls_proctermino
0 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| gls_proctermino | character | 0 | 1 | 7 | 0 | 7 | 27 | 0 |
gls_tipo_imputado
Distribution
Distribution of values for gls_tipo_imputado
0 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| gls_tipo_imputado | character | 0 | 1 | 6 | 0 | 7 | 11 | 0 |
iddelito
Distribution
Distribution of values for iddelito
0 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | label |
|---|---|---|---|---|---|---|---|---|---|---|
| iddelito | numeric | 0 | 1 | 34145 | 13348410 | 23697821 | 13437431 | 4192444 | <U+2581><U+2585><U+2587><U+2587><U+2583> |
fec_comision
Distribution
## 342509 unique, categorical values, so not shown.0 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | min | median | max | label |
|---|---|---|---|---|---|---|---|---|
| fec_comision | POSIXct | 0 | 1 | 342509 | 1900-01-01 | 2014-04-22 18:30:00 | 2019-12-31 06:00:00 |
termino_relacion
Distribution
## 4346 unique, categorical values, so not shown.21 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | min | median | max | label |
|---|---|---|---|---|---|---|---|---|
| termino_relacion | POSIXct | 21 | 0.999963 | 4346 | 2002-10-24 | 2014-11-13 | 2021-07-20 |
lugar_ocurrencia
Distribution
Distribution of values for lugar_ocurrencia
3866 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| lugar_ocurrencia | character | 3866 | 0.9931972 | 9 | 0 | 25 | 61 | 0 |
gls_sitiosuceso
Distribution
Distribution of values for gls_sitiosuceso
4803 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| gls_sitiosuceso | character | 4803 | 0.9915484 | 54 | 0 | 3 | 32 | 0 |
gls_comuna
Distribution
Distribution of values for gls_comuna
1048 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| gls_comuna | character | 1048 | 0.9981559 | 346 | 0 | 4 | 20 | 0 |
region_delito
Distribution
Distribution of values for region_delito
568158 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| region_delito | character | 568158 | 0.0002446 | 4 | 0 | 6 | 15 | 0 |
fec_cbiorelacion
Distribution
## 43966 unique, categorical values, so not shown.0 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | min | median | max | label |
|---|---|---|---|---|---|---|---|---|
| fec_cbiorelacion | POSIXct | 0 | 1 | 43966 | 2002-10-24 | 2014-11-13 | 2021-07-20 |
medidas_155
Distribution
Distribution of values for medidas_155
510920 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| medidas_155 | character | 510920 | 0.1009631 | 1 | 0 | 2 | 2 | 0 |
medidas_pp
Distribution
Distribution of values for medidas_pp
546001 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| medidas_pp | character | 546001 | 0.039233 | 1 | 0 | 2 | 2 | 0 |
medidas_ip
Distribution
Distribution of values for medidas_ip
567564 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| medidas_ip | character | 567564 | 0.0012898 | 1 | 0 | 2 | 2 | 0 |
marca_suspension_43
Distribution
Distribution of values for marca_suspension_43
498761 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| marca_suspension_43 | character | 498761 | 0.1223586 | 2 | 0 | 2 | 2 | 0 |
marca_pena_44
Distribution
Distribution of values for marca_pena_44
498761 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| marca_pena_44 | character | 498761 | 0.1223586 | 1 | 0 | 2 | 2 | 0 |
marca_multa_45
Distribution
Distribution of values for marca_multa_45
498761 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| marca_multa_45 | character | 498761 | 0.1223586 | 2 | 0 | 2 | 2 | 0 |
medida_alternativa_46
Distribution
Distribution of values for medida_alternativa_46
527687 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| medida_alternativa_46 | character | 527687 | 0.0714591 | 4 | 0 | 4 | 20 | 0 |
clasificacion_pena_47
Distribution
Distribution of values for clasificacion_pena_47
498761 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| clasificacion_pena_47 | character | 498761 | 0.1223586 | 9 | 0 | 7 | 28 | 0 |
tramos_condena_48
Distribution
## Error in if (stats::median(table(x)) == 1) {: valor ausente donde TRUE/FALSE es necesario## No non-missing values to show.568297 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | count | label |
|---|---|---|---|---|---|
| tramos_condena_48 | logical | 568297 | 0 | : |
clasificacion_penarpa_1_49
Distribution
## Error in if (stats::median(table(x)) == 1) {: valor ausente donde TRUE/FALSE es necesario## No non-missing values to show.568297 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | count | label |
|---|---|---|---|---|---|
| clasificacion_penarpa_1_49 | logical | 568297 | 0 | : |
clasificacion_penarpa_2_50
Distribution
## Error in if (stats::median(table(x)) == 1) {: valor ausente donde TRUE/FALSE es necesario## No non-missing values to show.568297 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | count | label |
|---|---|---|---|---|---|
| clasificacion_penarpa_2_50 | logical | 568297 | 0 | : |
marca_suspension_51
Distribution
Distribution of values for marca_suspension_51
568220 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| marca_suspension_51 | character | 568220 | 0.0001355 | 2 | 0 | 2 | 2 | 0 |
marca_pena_52
Distribution
Distribution of values for marca_pena_52
568220 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| marca_pena_52 | character | 568220 | 0.0001355 | 1 | 0 | 2 | 2 | 0 |
marca_multa_53
Distribution
Distribution of values for marca_multa_53
568220 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| marca_multa_53 | character | 568220 | 0.0001355 | 2 | 0 | 2 | 2 | 0 |
medida_alternativa_54
Distribution
Distribution of values for medida_alternativa_54
568252 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| medida_alternativa_54 | character | 568252 | 0.0000792 | 4 | 0 | 4 | 20 | 0 |
clasificacion_pena_55
Distribution
Distribution of values for clasificacion_pena_55
568220 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace | label |
|---|---|---|---|---|---|---|---|---|---|
| clasificacion_pena_55 | character | 568220 | 0.0001355 | 6 | 0 | 7 | 27 | 0 |
tramos_condena_56
Distribution
## Error in if (stats::median(table(x)) == 1) {: valor ausente donde TRUE/FALSE es necesario## No non-missing values to show.568297 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | count | label |
|---|---|---|---|---|---|
| tramos_condena_56 | logical | 568297 | 0 | : |
clasificacion_penarpa_1_57
Distribution
## Error in if (stats::median(table(x)) == 1) {: valor ausente donde TRUE/FALSE es necesario## No non-missing values to show.568297 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | count | label |
|---|---|---|---|---|---|
| clasificacion_penarpa_1_57 | logical | 568297 | 0 | : |
clasificacion_penarpa_2_58
Distribution
## Error in if (stats::median(table(x)) == 1) {: valor ausente donde TRUE/FALSE es necesario## No non-missing values to show.568297 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | count | label |
|---|---|---|---|---|---|
| clasificacion_penarpa_2_58 | logical | 568297 | 0 | : |
fec_comision_simple
Distribution
## 5574 unique, categorical values, so not shown.0 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | min | median | max | label |
|---|---|---|---|---|---|---|---|---|
| fec_comision_simple | Date | 0 | 1 | 5574 | 1900-01-01 | 2014-04-22 | 2019-12-31 |
fec_cbiorelacion_simple
Distribution
## 4346 unique, categorical values, so not shown.0 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | min | median | max | label |
|---|---|---|---|---|---|---|---|---|
| fec_cbiorelacion_simple | Date | 0 | 1 | 4346 | 2002-10-24 | 2014-11-13 | 2021-07-20 |
termino_relacion_simple
Distribution
## 4346 unique, categorical values, so not shown.21 missing values.
Summary statistics
| name | data_type | n_missing | complete_rate | n_unique | min | median | max | label |
|---|---|---|---|---|---|---|---|---|
| termino_relacion_simple | Date | 21 | 0.999963 | 4346 | 2002-10-24 | 2014-11-13 | 2021-07-20 |
Missingness report
Codebook table
JSON-LD metadata
The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.
{
"name": "Prosecutor Support System (SAF), database of the Public Prosecutor's Office.",
"description": "The information was extracted and processed from the Prosecutor Support System (SAF) database in accordance with the criteria established in the methodological document 'Criteria for Extracting Information from the SAF - 11_04_2018', prepared and updated in 2018 by the Studies, Evaluation, Control and Management Development Division. Data obtained at 2021-08-13; Period= 2010 to 2019; Unit of measurement= Relationships; Offenses considered= All; Date to consider= Date of termination of the case; National Coverage; Detail or summary= Detail; In the search for victims, only direct victims were considered; The RUTs indicated are searched only in cases terminated or suspended in the period 2010 - 2019;\nThe following types of accused are considered: COMPLAINED, ACCUSED, QUERELLED, WITNESS, SUSPECTED AND INVESTIGATED\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name |label | n_missing|\n|:--------------------------|:-----|---------:|\n|encontrado_como_victima | | 0|\n|encontrado_como_imputado | | 0|\n|tipo_sujeto_vic | | 18053|\n|gls_tipo_sujeto_vic | | 18053|\n|idsujeto_victima | | 0|\n|reg | | 0|\n|gls_region | | 317781|\n|tipo_termino | | 21|\n|agrupa_terminos | | 21|\n|gls_materia | | 0|\n|familia_delito | | 0|\n|relacion_vifsaf | | 0|\n|gls_parentesco | | 452460|\n|gls_mottermino | | 10353|\n|gls_motsuspension | | 493206|\n|gls_proctermino | | 0|\n|gls_tipo_imputado | | 0|\n|iddelito | | 0|\n|fec_comision | | 0|\n|termino_relacion | | 21|\n|lugar_ocurrencia | | 3866|\n|gls_sitiosuceso | | 4803|\n|gls_comuna | | 1048|\n|region_delito | | 568158|\n|fec_cbiorelacion | | 0|\n|medidas_155 | | 510920|\n|medidas_pp | | 546001|\n|medidas_ip | | 567564|\n|marca_suspension_43 | | 498761|\n|marca_pena_44 | | 498761|\n|marca_multa_45 | | 498761|\n|medida_alternativa_46 | | 527687|\n|clasificacion_pena_47 | | 498761|\n|tramos_condena_48 | | 568297|\n|clasificacion_penarpa_1_49 | | 568297|\n|clasificacion_penarpa_2_50 | | 568297|\n|marca_suspension_51 | | 568220|\n|marca_pena_52 | | 568220|\n|marca_multa_53 | | 568220|\n|medida_alternativa_54 | | 568252|\n|clasificacion_pena_55 | | 568220|\n|tramos_condena_56 | | 568297|\n|clasificacion_penarpa_1_57 | | 568297|\n|clasificacion_penarpa_2_58 | | 568297|\n|fec_comision_simple | | 0|\n|fec_cbiorelacion_simple | | 0|\n|termino_relacion_simple | | 21|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
"datePublished": "2021-11-16",
"keywords": ["encontrado_como_victima", "encontrado_como_imputado", "tipo_sujeto_vic", "gls_tipo_sujeto_vic", "idsujeto_victima", "reg", "gls_region", "tipo_termino", "agrupa_terminos", "gls_materia", "familia_delito", "relacion_vifsaf", "gls_parentesco", "gls_mottermino", "gls_motsuspension", "gls_proctermino", "gls_tipo_imputado", "iddelito", "fec_comision", "termino_relacion", "lugar_ocurrencia", "gls_sitiosuceso", "gls_comuna", "region_delito", "fec_cbiorelacion", "medidas_155", "medidas_pp", "medidas_ip", "marca_suspension_43", "marca_pena_44", "marca_multa_45", "medida_alternativa_46", "clasificacion_pena_47", "tramos_condena_48", "clasificacion_penarpa_1_49", "clasificacion_penarpa_2_50", "marca_suspension_51", "marca_pena_52", "marca_multa_53", "medida_alternativa_54", "clasificacion_pena_55", "tramos_condena_56", "clasificacion_penarpa_1_57", "clasificacion_penarpa_2_58", "fec_comision_simple", "fec_cbiorelacion_simple", "termino_relacion_simple"],
"@context": "http://schema.org/",
"@type": "Dataset",
"variableMeasured": [
{
"name": "encontrado_como_victima",
"@type": "propertyValue"
},
{
"name": "encontrado_como_imputado",
"@type": "propertyValue"
},
{
"name": "tipo_sujeto_vic",
"@type": "propertyValue"
},
{
"name": "gls_tipo_sujeto_vic",
"@type": "propertyValue"
},
{
"name": "idsujeto_victima",
"@type": "propertyValue"
},
{
"name": "reg",
"@type": "propertyValue"
},
{
"name": "gls_region",
"@type": "propertyValue"
},
{
"name": "tipo_termino",
"@type": "propertyValue"
},
{
"name": "agrupa_terminos",
"@type": "propertyValue"
},
{
"name": "gls_materia",
"@type": "propertyValue"
},
{
"name": "familia_delito",
"@type": "propertyValue"
},
{
"name": "relacion_vifsaf",
"@type": "propertyValue"
},
{
"name": "gls_parentesco",
"@type": "propertyValue"
},
{
"name": "gls_mottermino",
"@type": "propertyValue"
},
{
"name": "gls_motsuspension",
"@type": "propertyValue"
},
{
"name": "gls_proctermino",
"@type": "propertyValue"
},
{
"name": "gls_tipo_imputado",
"@type": "propertyValue"
},
{
"name": "iddelito",
"@type": "propertyValue"
},
{
"name": "fec_comision",
"@type": "propertyValue"
},
{
"name": "termino_relacion",
"@type": "propertyValue"
},
{
"name": "lugar_ocurrencia",
"@type": "propertyValue"
},
{
"name": "gls_sitiosuceso",
"@type": "propertyValue"
},
{
"name": "gls_comuna",
"@type": "propertyValue"
},
{
"name": "region_delito",
"@type": "propertyValue"
},
{
"name": "fec_cbiorelacion",
"@type": "propertyValue"
},
{
"name": "medidas_155",
"@type": "propertyValue"
},
{
"name": "medidas_pp",
"@type": "propertyValue"
},
{
"name": "medidas_ip",
"@type": "propertyValue"
},
{
"name": "marca_suspension_43",
"@type": "propertyValue"
},
{
"name": "marca_pena_44",
"@type": "propertyValue"
},
{
"name": "marca_multa_45",
"@type": "propertyValue"
},
{
"name": "medida_alternativa_46",
"@type": "propertyValue"
},
{
"name": "clasificacion_pena_47",
"@type": "propertyValue"
},
{
"name": "tramos_condena_48",
"@type": "propertyValue"
},
{
"name": "clasificacion_penarpa_1_49",
"@type": "propertyValue"
},
{
"name": "clasificacion_penarpa_2_50",
"@type": "propertyValue"
},
{
"name": "marca_suspension_51",
"@type": "propertyValue"
},
{
"name": "marca_pena_52",
"@type": "propertyValue"
},
{
"name": "marca_multa_53",
"@type": "propertyValue"
},
{
"name": "medida_alternativa_54",
"@type": "propertyValue"
},
{
"name": "clasificacion_pena_55",
"@type": "propertyValue"
},
{
"name": "tramos_condena_56",
"@type": "propertyValue"
},
{
"name": "clasificacion_penarpa_1_57",
"@type": "propertyValue"
},
{
"name": "clasificacion_penarpa_2_58",
"@type": "propertyValue"
},
{
"name": "fec_comision_simple",
"@type": "propertyValue"
},
{
"name": "fec_cbiorelacion_simple",
"@type": "propertyValue"
},
{
"name": "termino_relacion_simple",
"@type": "propertyValue"
}
]
}`Session Info
Sys.getenv("R_LIBS_USER")## [1] "C:/Users/CISS Fondecyt/OneDrive/Documentos/R/win-library/4.0"rstudioapi::getSourceEditorContext()## Document Context:
## - id: 'AFE93741'
## - path: 'C:/Users/CISS Fondecyt/Mi unidad/Alvacast/SISTRAT 2019 (github)/SUD_CL/Fiscalia_merge.Rmd'
## - contents: <1686 rows>
## Document Selection:
## - [480, 58] -- [480, 58]: ''#save.image("G:/Mi unidad/Alvacast/SISTRAT 2019 (github)/mult_state_carla.RData")
if (grepl("CISS Fondecyt",rstudioapi::getSourceEditorContext()$path)==T){
save.image("C:/Users/CISS Fondecyt/Mi unidad/Alvacast/SISTRAT 2019 (github)/10.RData")
} else if (grepl("andre",rstudioapi::getSourceEditorContext()$path)==T){
save.image("C:/Users/andre/Desktop/SUD_CL/10.RData")
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save.image("E:/Mi unidad/Alvacast/SISTRAT 2019 (github)/10.RData")
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sessionInfo()## R version 4.0.2 (2020-06-22)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] future_1.21.0 ggrepel_0.8.2 compareGroups_4.5.1
## [4] readxl_1.3.1 radiant.update_1.4.1 ggside_0.1.3
## [7] ggstatsplot_0.9.0 flextable_0.6.7 ClusterR_1.2.5
## [10] gtools_3.9.2 polycor_0.7-10 caret_6.0-88
## [13] rpivotTable_0.3.0 Metrics_0.1.4 muhaz_1.2.6.4
## [16] flexsurv_2.0 mstate_0.3.1 DiagrammeR_1.0.6.1.9000
## [19] Amelia_1.8.0 Rcpp_1.0.7 igraph_1.2.6
## [22] eha_2.9.0 cobalt_4.3.1 MatchIt_4.2.0
## [25] tableone_0.13.0 stargazer_5.2.2 reshape2_1.4.4
## [28] gridExtra_2.3 foreign_0.8-81 survMisc_0.5.5
## [31] ggfortify_0.4.12 survminer_0.4.9 ggpubr_0.4.0
## [34] epiR_2.0.33 forcats_0.5.1 purrr_0.3.4
## [37] readr_2.0.1 tibble_3.0.3 tidyverse_1.3.1
## [40] dplyr_1.0.7 treemapify_2.5.5 sf_1.0-2
## [43] ggiraph_0.7.10 finalfit_1.0.3 lsmeans_2.30-0
## [46] emmeans_1.6.3 RColorBrewer_1.1-2 panelr_0.7.5
## [49] lme4_1.1-27.1 Matrix_1.2-18 data.table_1.14.2
## [52] codebook_0.9.2 devtools_2.4.2 usethis_2.0.1
## [55] sqldf_0.4-11 RSQLite_2.2.8 gsubfn_0.7
## [58] proto_1.0.0 broom_0.7.9 zoo_1.8-9
## [61] rbokeh_0.5.2 janitor_2.1.0 plotly_4.9.4.1
## [64] kableExtra_1.3.4 Hmisc_4.5-0 Formula_1.2-4
## [67] survival_3.1-12 lattice_0.20-41 ggplot2_3.3.5
## [70] stringr_1.4.0 stringi_1.4.6 tidyr_1.1.3
## [73] knitr_1.33 matrixStats_0.60.0 boot_1.3-28
##
## loaded via a namespace (and not attached):
## [1] svglite_2.0.0 class_7.3-19 ps_1.6.0
## [4] foreach_1.5.1 rprojroot_2.0.2 crayon_1.4.1
## [7] MASS_7.3-51.6 PMCMRplus_1.9.2 nlme_3.1-148
## [10] backports_1.1.7 reprex_2.0.1 rlang_0.4.11
## [13] performance_0.8.0 nloptr_1.2.2.2 callr_3.7.0
## [16] bit64_4.0.5 glue_1.4.1 sjPlot_2.8.9
## [19] parallel_4.0.2 processx_3.5.2 classInt_0.4-3
## [22] tcltk_4.0.2 haven_2.4.3 tidyselect_1.1.1
## [25] km.ci_0.5-2 rio_0.5.27 sjmisc_2.8.7
## [28] SuppDists_1.1-9.5 mc2d_0.1-21 chron_2.3-56
## [31] xtable_1.8-4 magrittr_2.0.1 evaluate_0.14
## [34] gdtools_0.2.3 cli_3.0.1 rstudioapi_0.13
## [37] bslib_0.2.5.1 rpart_4.1-15 jtools_2.1.3
## [40] sjlabelled_1.1.8 maps_3.3.0 gistr_0.9.0
## [43] xfun_0.25 parameters_0.15.0 pkgbuild_1.2.0
## [46] cluster_2.1.2 WRS2_1.1-3 ggfittext_0.9.1
## [49] listenv_0.8.0 png_0.1-7 reshape_0.8.8
## [52] ipred_0.9-11 zeallot_0.1.0 withr_2.4.2
## [55] plyr_1.8.6 cellranger_1.1.0 e1071_1.7-8
## [58] pROC_1.17.0.1 survey_4.1-1 coda_0.19-4
## [61] pillar_1.6.2 cachem_1.0.6 Rmpfr_0.8-7
## [64] multcomp_1.4-17 fs_1.5.0 paletteer_1.4.0
## [67] vctrs_0.3.8 ellipsis_0.3.2 generics_0.1.0
## [70] lava_1.6.9 tools_4.0.2 munsell_0.5.0
## [73] proxy_0.4-26 fastmap_1.1.0 compiler_4.0.2
## [76] pkgload_1.2.1 abind_1.4-5 sessioninfo_1.1.1
## [79] prodlim_2019.11.13 visNetwork_2.0.9 utf8_1.1.4
## [82] recipes_0.1.16 jsonlite_1.7.2 scales_1.1.1
## [85] carData_3.0-4 estimability_1.3 lazyeval_0.2.2
## [88] car_3.0-11 BWStest_0.2.2 latticeExtra_0.6-29
## [91] effectsize_0.5 checkmate_2.0.0 rmarkdown_2.10
## [94] openxlsx_4.2.4 sandwich_3.0-1 webshot_0.5.2
## [97] pander_0.6.3 numDeriv_2016.8-1.1 yaml_2.2.1
## [100] systemfonts_1.0.2 htmltools_0.5.1.1 memoise_2.0.0
## [103] quadprog_1.5-8 viridisLite_0.4.0 gmp_0.6-2
## [106] digest_0.6.25 assertthat_0.2.1 repr_1.1.3
## [109] bayestestR_0.11.5 BiasedUrn_1.07 KMsurv_0.1-5
## [112] units_0.7-2 remotes_2.4.0 blob_1.2.2
## [115] labeling_0.4.2 deSolve_1.28 splines_4.0.2
## [118] rematch2_2.1.2 hms_1.1.0 modelr_0.1.8
## [121] colorspace_1.4-1 base64enc_0.1-3 nnet_7.3-16
## [124] sass_0.4.0 mvtnorm_1.1-2 multcompView_0.1-8
## [127] fansi_0.4.1 tzdb_0.1.2 parallelly_1.27.0
## [130] truncnorm_1.0-8 ModelMetrics_1.2.2.2 R6_2.5.1
## [133] grid_4.0.2 crul_1.1.0 lifecycle_1.0.0
## [136] labelled_2.8.0 statsExpressions_1.2.0 writexl_1.3
## [139] zip_2.2.0 datawizard_0.2.1 curl_4.3
## [142] ggsignif_0.6.3 pryr_0.1.5 minqa_1.2.4
## [145] testthat_3.0.4 jquerylib_0.1.4 snakecase_0.11.0
## [148] skimr_2.1.3 desc_1.3.0 TH.data_1.0-10
## [151] iterators_1.0.13 gower_0.2.2 htmlwidgets_1.5.3.9000
## [154] officer_0.3.19 crosstalk_1.1.1 rvest_1.0.1
## [157] globals_0.14.0 insight_0.14.5 htmlTable_2.2.1
## [160] patchwork_1.1.1 codetools_0.2-16 lubridate_1.7.10
## [163] prettyunits_1.1.1 dbplyr_2.1.1 correlation_0.7.1
## [166] gtable_0.3.0 DBI_1.1.1 stats4_4.0.2
## [169] highr_0.9 httr_1.4.2 KernSmooth_2.23-17
## [172] kSamples_1.2-9 farver_2.0.3 uuid_0.1-4
## [175] rmdpartials_0.5.8 hexbin_1.28.2 timeDate_3043.102
## [178] mice_3.13.0 DT_0.18 xml2_1.3.2
## [181] ggeffects_1.1.1 bit_4.0.4 sjstats_0.18.1
## [184] jpeg_0.1-9 pkgconfig_2.0.3 rstatix_0.7.0
## [187] mitools_2.4 HardyWeinberg_1.7.2 Rsolnp_1.16
## [190] httpcode_0.3.0Exportar
if(no_mostrar==0){
codebook::var_label(surveymonkey_accionsalududp_df2_cor_sel_2) <-
var_labels_df%>%
codebook::dict_to_list()
attr(surveymonkey_accionsalududp_df2_cor_sel_2$rn,"label")<-'Número de Fila'
for (i in 1:length(surveymonkey_accionsalududp_df2_cor_table)){
x<-names(surveymonkey_accionsalududp_df2_cor_table)[i]
attr(surveymonkey_accionsalududp_df2_cor_sel_2[[x]],"label")<- attr(surveymonkey_accionsalududp_df2_cor_table[[x]],"label")
var_labels_df<-data.frame()
for (i in 1:length(surveymonkey_accionsalududp_df2_cor_sel_2)){
x<-names(surveymonkey_accionsalududp_df2_cor_sel_2)[i]
var_labels_df<-rbind.data.frame(var_labels_df, cbind(x, attr(surveymonkey_accionsalududp_df2_cor_sel_2[[x]],"label")))
}
}
}
#dplyr::arrange(hash_key, fech_ing)%>%
path<-rstudioapi::getSourceEditorContext()$path
base_fiscalia %>%
dplyr::mutate(accused=dplyr::case_when(encontrado_como_victima=="SI" & encontrado_como_imputado=="SI"~"IMPUTADO",
encontrado_como_victima=="NO" & encontrado_como_imputado=="SI"~"IMPUTADO",
T~"VICTIMA")) %>%
dplyr::filter(accused=="VICTIMA") %>%
rio::export(file = paste0(gsub("SUD_CL/Fiscalia_merge.Rmd","",path),"fiscalia_mariel_ago_2021.dta"))